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Guided Structural Inference: Leveraging Priors with Soft Gating Mechanisms | Accept (poster) | Summary: The paper introduces a more controlled method for structural inference that allow us to imposed a series of constrained to the predicted structure. Specifically, it enables conditioning on a set of edges that must exist in the structure, enforcing the absence of certain edges, and controlling sparsity in terms... | Rebuttal 1:
Rebuttal: We thank you for your detailed review and constructive feedback. Below, we address your concerns (detailed tables can be found at https://anonymous.4open.science/r/SGSI-Rebuttal-1614/Tables.pdf ):
**1. Choice of Evaluation Metrics and Downstream Tasks:**
We selected AUROC as our primary metric b... | Summary: This paper proposes Soft-Gated Structural Inference (SGSI), a framework to solve the task of infer latent relational structures where additional prior knowledge can be incorporated. Theoretical analysis and experiments verify the effectiveness of the proposed method.
Claims And Evidence: Yes
Methods And Eval... | Rebuttal 1:
Rebuttal: We thank you for your detailed review and the constructive feedback regarding SGSI. We address your concerns below (tables can be found at https://anonymous.4open.science/r/SGSI-Rebuttal-1614/Tables.pdf ):
**1. Importance of Prior Knowledge**
Our experiments across multiple benchmarks, including... | Summary: The paper "Guided Structural Inference: Leveraging Priors with Soft Gating Mechanisms" introduces Soft-Gated Structural Inference (SGSI), a variational autoencoder (VAE)-based method for inferring latent relational structures while integrating domain constraints.
Claims And Evidence: Yes
Methods And Evaluati... | Rebuttal 1:
Rebuttal: We appreciate your constructive feedback regarding our paper SGSI. Below, we address your concerns point by point:
**1. Marginal Gains on Certain Datasets**
While SGSI shows up to 9% AUROC improvement on some datasets, other cases yield more modest gains (1–2%). We believe this reflects two fact... | Summary: The paper introduces SGSI, a framework for latent graph structure learning that integrates partial prior knowledge into a VAE. It employs a soft gating mechanism with learnable parameters to smoothly control edge activation and uses a cloning and clamping strategy to fix known-present and known-absent edges wi... | Rebuttal 1:
Rebuttal: We thank you for your positive assessment and valuable comments. In response, we clarify our approach as follows (tables can be found at https://anonymous.4open.science/r/SGSI-Rebuttal-1614/Tables.pdf .)
**1. Hyperparameters**
SGSI introduces hyperparameters, most notably, the KL weight $\beta$,... | null | null | null | null | null | null |
Cavia: Camera-controllable Multi-view Video Diffusion with View-Integrated Attention | Accept (poster) | Summary: The authors propose a novel framework of image-to-multi-view video generation, with controllable cameras. To achieve this, the design a flexible multi-frame/multi-view attention module, allowing for joint training of static scene video, monocular video and multi-view dynamic scene video. Experiments on monocul... | Rebuttal 1:
Rebuttal: We thank the reviewer T9u5 for their detailed comments and constructive suggestions. However, we respectfully disagree with the reviewer's concerns regarding our contribution. In response, we have added additional comparisons against concurrent works ViewCrafter and CVD, which are available on our... | Summary: This paper introduced a multi-view video diffusion model enhanced by view-integrated attention, called Cavia. Specifically, Cavia used cross-view attention and cross-frame attention to ensure multi-view and temporal consistency respectively. This model design also enabled Cavia to train jointly with diverse da... | Rebuttal 1:
Rebuttal: We thank the reviewer vi5f for their valuable effort and for recognizing the strength of our experimental results. However, we respectfully disagree with the concerns regarding "large viewpoint changes like SynCamMaster." We would like to clarify that **the concurrent work SynCamMaster is limited ... | Summary: This paper proposed a novel framework for camera-controlled multi-view video generation.
Based on SVD, it proposes to use 3D attention in both frame attention and view attention to ensure spatio-temporal consistency. In addition, it curated a mixed dataset from a lot of real and synthetic datasets for the m... | Rebuttal 1:
Rebuttal: We thank reviewer KtKL for the detailed comments and for recognizing our strong performance compared to existing works. Below are our detailed responses to the questions raised.
**Q1. Baseline details are missing: 1) How did SVD achieve camera pose? 2) How are MotionCtrl and CameraCtrl implemente... | Summary: This paper introduces a novel framework named Cavia for generating multi-view videos with camera controllability.
The primary contributions consist of two key components: 1) Cross-view and cross-frame 3D attention mechanisms designed to enhance consistency across different viewpoints and temporal frames. 2) A... | Rebuttal 1:
Rebuttal: We thank the reviewer RKXa for their positive evaluation of the technical novelty and superior performance of our method. As suggested, we will enhance the presentation of our method and figures in the revised draft. Formal mathematical equations will be added to clarify the computation of the pro... | null | null | null | null | null | null |
Stability and Generalization Analysis of Decentralized SGD: Sharper Bounds Beyond Lipschitzness and Smoothness | Accept (poster) | Summary: This work establishes sharper stability and generalization bounds for decentralized SGD (D-SGD) under weaker assumptions. The analysis primarily builds on the on-average model stability, with a key innovation lying in the novel decomposition of neighboring consensus errors in decentralized settings.
Claims An... | Rebuttal 1:
Rebuttal: We thank you for taking the time to review our paper and greatly appreciate your valuable feedback.
**Q1**: The theoretical analysis in this paper is limited to the convex problem. Can the analysis in this paper be extended to non-convex problems, e.g., training deep neural networks, and does the... | Summary: This work studies decentralized stochastic gradient descent where a network of agents collaborate to minimize an aggregate of local cost functions privately available to each agent. It focuses on the generalization analysis, which is different from the convergence rate analysis. It improves the generalization ... | Rebuttal 1:
Rebuttal: We thank you for taking the time to review our paper and greatly appreciate your valuable feedback.
**Q1**: Lack of clarity in presenting the problem and results. Notation is quite confusing.
**A**: Thanks for your valuable feedback. We will reorganize the problem statement more clearly, systema... | Summary: This paper studies the stability of D-SGD, and presents new and sharper convergence bounds on the assumption that the functions are convex and L-smooth. The removal of the function Lipschitzness and the bounded variance assumptions highlight the novelty of the work. Theoretical analysis shows an improved stabi... | Rebuttal 1:
Rebuttal: We thank you for taking the time to review our paper and greatly appreciate your valuable feedback.
**Q1**: The theoretical claims are mostly to be expected and seem to be either an extension of previous D-SGD analysis or from the stability/generalization of SGD in the centralized setting.
**A**... | Summary: This paper presents the generalization and excess risk analysis for decentralized stochastic gradient descent (D-SGD) on smooth (including Hölder continuous, which generalizes smoothness) and convex problems. The key contribution is the removal of the standard Lipschitzness assumption in the analysis. The auth... | Rebuttal 1:
Rebuttal: We thank you for taking the time to review our paper and greatly appreciate your valuable feedback.
**Q1**: Exploring nonconvex settings could further enhance the practical relevance of the results.
**A**: Thanks for the suggestion. We agree that generalization analysis for nonconvex problems is... | null | null | null | null | null | null |
QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration | Accept (poster) | Summary: This paper addresses the challenge of efficiently serving multiple fine-tuned MoE on a single GPU. The authors propose a novel serving system with two key components: Similarity-based expert consolidation and Runtime partial reconfiguration.
The authors evaluate their approach using Mixtral-8x7B models on a se... | Rebuttal 1:
Rebuttal: Thank you for your invaluable comments. The experiments and additional explanations provided here have been incorporated into the revised manuscript.
Weaknesses:
W1: The approach is demonstrated only for two models from the same model family (Mixtral base and instruct variants). It's unclear ... | Summary: This paper presents a novel serving system for multiple fine-tuned Mixture-of-Expert (MoE) Large Language Models (LLMs) on a single GPU. The approach uses similarity-based expert consolidation to share similar experts across models, coupled with runtime partial reconfiguration to dynamically replace non-expert... | Rebuttal 1:
Rebuttal: Thank you for your invaluable comments. The experiments and additional explanations provided here have been incorporated into the revised manuscript.
Weaknesses:
W1: Limited analysis of scaling behavior with >2 models:
Response 1: To demonstrate the scalability and applicability of our appro... | Summary: Updated score 2->3 after rebuttal.
-----------------------
The paper proposes a novel serving system to address the problem of efficiently serving multiple finetuned mixture-of-experts (MoE) large language models (LLMs). The idea is to run a similarity based expert consolidation to share similar experts acro... | Rebuttal 1:
Rebuttal: Thank you for your invaluable comments. The experiments and additional explanations provided here have been incorporated into the revised manuscript.
Weaknesses:
W1: I mentioned above about using only ...
Response 1:
*For a detailed response with numerical results, please refer to “Response 1” ... | null | null | null | null | null | null | null | null |
Multivariate Conformal Selection | Accept (poster) | Summary: This paper introduces Multivariate Conformal Selection (mCS), a new approach for selecting candidates in settings with multivariate responses, such as drug discovery and large language model alignment. Unlike traditional Conformal Selection, which is limited to single-variable outputs, mCS extends the framewor... | Rebuttal 1:
Rebuttal: > On page 12, just before "The last equality is again by", $p\_{j}^\* \in \mathcal S\_{j}^\*$ should be replaced with $j \in \mathcal S\_{j}^*$... since the p-value corresponding to $j$ in $S^{\*}\_{j\to0}$ is 0.
**A1**: Thank you very much for pointing out the typo and the error in our proof. We... | Summary: This paper addresses the important problem of multivariate conformal selection. While multivariate conformal prediction is relatively well studied, this appears to be the first work on multivariate selection tasks.
## update after rebuttal: I did not change my score as the recommendation is already to accept... | Rebuttal 1:
Rebuttal: > It would be good to see an evaluation on classification outcomes as well.
**A1**:
In our paper, we chose to focus primarily on regression tasks because they represent a more challenging setting for conformal selection.
In fact, the selection problem for classification (univariate or multivari... | Summary: This paper proposes multivariate conformal selection (mCS), which extends conformal selection to multi-response settings by introducing the concept of regional monotonicity, generalizing univariate monotonicity, and defining multivariate non-conformity scores. mCS guarantees finite-sample false discovery rate ... | Rebuttal 1:
Rebuttal: > The method requires an extra split... in higher dim.
**A1**: From our experience, the (multivariate) conformal selection procedure is insensitive to the size of calibration data - the calibration scores only affect the resolution of the p-values, which is typically sufficient when $|D\_{cal}| \... | null | null | null | null | null | null | null | null |
Enhancing Graph Contrastive Learning for Protein Graphs from Perspective of Invariance | Accept (poster) | Summary: This paper proposes a novel framework for protein representation learning by introducing two biologically informed graph-augmentation strategies for contrastive learning. Specifically, it combines:
1. Functional Community Invariance (FCI), which preserves crucial residue clusters (communities) involved in pr... | Rebuttal 1:
Rebuttal: **We sincerely thank reviewer UCwy for detailed reading and meaningful feedback.**
***
> **_Q1_** *Not against large-scale pretrained language models.*
**A1** In Appendix F.2, we have provided comparisons with protein LMs like ESM-1b. For extended experiments, like comparison to ESM-2, you can re... | Summary: This paper improves on the Graph Contrastive Learning (GCL) by introducing two graph augmentation techniques: Functional Community Invariance (FCI) and 3D Protein Structure Invariance (3-PSI). These augmentation techniques are designed to preserve the functional and structural integrity of proteins. The author... | Rebuttal 1:
Rebuttal: **We are grateful to reviewer ZGC5 for insightful reviews.**
***
> **__Q1__** *Did not describe the GNN encoder. In the main text, the authors...in Appendix C4...why GNN is chosen.*
**A1** We sincerely apologize for lack of clarity. We employ GNNs because GNNs can flexibly integrate a protein’s t... | Summary: This paper introduces novel biology-aware graph augmentation strategies for protein representation learning within a Graph Contrastive Learning (GCL) framework. The authors identify limitations in existing GCL approaches that either focus exclusively on 2D topology (neglecting intrinsic biological properties) ... | Rebuttal 1:
Rebuttal: **We thank reviewer s9oD for the constructive feedback and valuable suggestions.**
***
> **__Q1__** *Incorporating comparisons with relevant models.*
**A1** We have conducted extensive experiments with the other suggested baselines shown as follows.
|Model|EC|GO-BP|GO-MF|GO-CC|FOLD-fold|FOLD-Sup... | Summary: This paper investigates methods to improve Graph Contrastive Learning for protein representation learning by incorporating biologically-aware graph augmentation strategies. The authors propose two novel augmentation strategies: Functional Community Invariance (FCI) and 3D Protein Structure Invariance (3-PSI), ... | Rebuttal 1:
Rebuttal: **We sincerely appreciate the reviewer exBg's thoughtful and valuable comments.**
***
> **_Q1_** *The derivation of both the upper and lower bounds ... requires clarification. & The weighted upper bound may need to be revised.*
**A1** We appreciate your valuable feedback on the proof. The proof ... | null | null | null | null | null | null |
WildChat-50M: A Deep Dive Into the Role of Synthetic Data in Post-Training | Accept (poster) | Summary: This paper constructs a larger and more high-quality post-training chat dataset (called WildChat-50M) by getting responses to prompts from more than just one "data-generating model" (DGM). The authors get responses to prompts from the WildChat-1M dataset from 50 open-weight models. The dataset contains over 1M... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful response. To the best of our ability, given the 5000 character limit, we address your comments and questions below.
We agree that our claim about SDQ depending primarily on prompt diversity could be worded more carefully. In the camera-ready draft, we will... | Summary: - The paper introduces WildChat-50M, the largest public chat dataset to date, featuring responses from 50+ different open-weight models (0.5B-104B parameters) participating in over 1M multi-turn conversations each.
- The authors created Re-Wild, a new supervised fine-tuning (SFT) data mix that outperforms All... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful response. To the best of our ability, we briefly address each of your comments and questions below.
To your point about confidence intervals; we agree that these would be valuable. Therefore, if our paper is accepted, we will add 95% confidence intervals u... | Summary: This paper constructed a large set of 50M synthetic conversations by using the initial human prompts from WildChat and various pretrained language models to generate responses and following turns. Based on the data, they further selected a subset and mixed with two other sources of data (MMLU Auxiliary Train a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful response. To the best of our ability, we briefly address each of your comments and questions below.
Regarding experiments to determine if synthetic data can surpass the performance of smaller-scale, higher-quality human-written chat responses: unfortunatel... | Summary: Authors are proposing a new synthetic dataset: 'WILDCHAT-50M'
Compare to other open datasets, 'WILDCHAT-50M' is much larger and includes synthetic data generated from many open source models other than GPT.
-WILDCHAT-50M is the largest public chat dataset to date
-It includes responses from over 50 different ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful response. To the best of our ability, we briefly address each of your comments and questions below.
You are correct that "wehow" is a typo and should read “we show”. We apologize for the inconvenience, and will remedy this in the camera-ready draft if we a... | Summary: The paper proposes a new synthetic chat dataset, WildChat-50M, which consists of generated responses from 50+ open weight models. The authors then created a new SFT datamix, Re-Wild, by combining WildChat-50M with two other datasets (MMLU Auxiliary Train, Tulu 3 Persona Hub Algebra). Main contribution of the p... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful response. To the best of our ability, we briefly address each of your comments and questions below.
To your point about our inability to include RLHF in this work (and therefore to answer your question about whether stronger SFT leads to stronger post-trai... | null | null | null | null |
MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking | Accept (poster) | Summary: The paper investigates the problem of multi-step reward hacking, in which an agent trained with an ordinary reinforcement learning algorithm, due to the statement of the RL optimization problem, learns to optimize for the sum of current and future rewards, potentially learning multi-step strategies to increase... | Rebuttal 1:
Rebuttal: We are happy you found the paper insightful, and thanks for the detailed comments on improving the clarity in the main paper.
We agree that the most important open question about MONA is what the safety-performance Pareto frontier looks like in practice and we’re hoping this will be studied in fu... | Summary: The paper introduces a method, called MONA, that mitigates multi-step reward hacking by limiting optimization to be myopic and adding a hand-crafted non-myopic approval reward. The paper provides three case studies to demonstrate how MONA can avoid multi-step reward hacking in comparison to ordinary RL and pro... | Rebuttal 1:
Rebuttal: Thanks for your review and the constructive criticism!
We broadly agree with the key limitations that your review identifies (and we discuss them in Appendix B). It will be important for future work to study the competitiveness of MONA and to test MONA in more realistic environments.
However, th... | Summary: This paper introduces an approach (MONA) to mitigating the risk of multi-step reward hacking (where an agent executes undesirable, multi-step plans to achieve high reward) by decomposing the reward function into a myopic task reward and an non-myopic approval reward. They demonstrate that this can help reduce ... | Rebuttal 1:
Rebuttal: Thanks for the review. We’re glad you like the paper!
We’d like to clarify the role of the approval feedback in MONA and in our experiments specifically, as this partially addresses the two concerns you brought up.
> I had an uneasy intuition that we were sort of just 'passing the buck' of rewar... | null | null | null | null | null | null | null | null |
Differentiable Structure Learning with Ancestral Constraints | Accept (poster) | Summary: The paper proposes a method to integrate prior knowledge of presence/absence of certain edges or paths into differentiable structural learning frameworks. The paper presents theoretical analyses of several strategies, and the related issues, for handling such constraints in a continuous optimization regime. Th... | Rebuttal 1:
Rebuttal: Thank you for your careful review. We first address your major concerns regarding novelty and technical differences. We abbreviate differentiable structure learning for causal discovery as DCD.
# Novelty over Partial Orders (Ban et al. (2024))
You consider path existence as part of partial orders... | Summary: This paper introduces a framework for integrating ancestral constraints into differentiable structure learning of causal DAGs, addressing challenges in representing path existence and order violations. The authors propose a binary-masked characterization method and an order-guided optimization strategy to impr... | Rebuttal 1:
Rebuttal: Thanks for your careful review. Here are our responses.
# Responses
2. Wang et al. formulate the path existence loss as $\text{ReLU}(\epsilon - |W|^k_{i,j})$ (Eq. (22) in their paper), assuming a known path length $k$. Without this assumption, the formulation naturally generalizes to $\text{ReLU... | Summary: The paper addresses the challenge of integrating ancestral constraints into differentiable structure learning for causal directed acyclic graphs (DAGs). The key problem is how to incorporate prior knowledge about the existence or absence of paths between variables (ancestral constraints) into the learning proc... | Rebuttal 1:
Rebuttal: Thanks for your detailed review and thoughtful comments. Here are our responses.
### 1. Non-Differentiability of Binary Mask
We provide empirical evidence that incorporating the binary masked path existence loss, $\bar{p}(W)\circ b(W)$, does not compromise optimization stability. To evaluate thi... | Summary: The paper addresses the problem of incorporating ancestral (path) constraints into differentiable causal structure learning methods, specifically NOTEARS-style algorithms. The authors identify two key issues with existing differentiable formulations: a non-equivalence issue in previous continuous relaxations o... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We will discuss the relevant references you mentioned in the paper. Here are our responses to your questions.
# Reply to Questions
1. The edge threshold $\epsilon_0$ is a standard parameter in differentiable structure learning, set to $0.3$ following the defau... | null | null | null | null | null | null |
Learning Gaussian DAG Models without Condition Number Bounds | Accept (poster) | Summary: In this paper, the authors revisit the problem of learning an $n$-variate Gaussian graphical model from samples. This problem is known to be solvable in $O(d\log n)$ samples where $d$ is the degree. However, there is a hidden polynomial dependence on the condition number of the covariance matrix of observation... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed response and thorough examination of our work. We answer the main points that were raised below.
>Although, it looks to me too few (only one) prior work has been compared against. Section E in the appendix had some more such comparisons. Maybe some of it c... | Summary: This submission investigates the estimation of Gaussian DAG structure from i.i.d. samples and under equal-variance assumption. The main findings are two-fold: the authors give a polynomial time algorithm to recover the structure based on ideas of sparse regression; the authors shows that the sample complexity ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging feedback and for pointing out these omissions, we will make sure to correct them in the final version. | Summary: This paper studies the sample complexity of learning linear Gaussian DAGs under equal variance assumption. It proposes a new algorithm and provides the graph recovery guarantee independent of the condition number. Both the upper and lower bounds of sample complexity are proved. The authors also provide simulat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging words and detailed feedback. Below we answer the main points that were raised.
# For comments:
>For figure 2, it would also be good to include the error bar.
Thank you very much for this suggestion. We will make sure to include the error bars in the f... | null | null | null | null | null | null | null | null |
Predicting High-precision Depth on Low-Precision Devices Using 2D Hilbert Curves | Accept (poster) | Summary: The paper presents an approach for neural network quantisation in monocular and binocular settings. The main idea is to decompose the high dynamic range depth into two low dynamic range components using a Hilbert curve and train a full precision DNN to predict these components. In practice, standard quantisati... | Rebuttal 1:
Rebuttal: We thank the reviewer QXvZ for his/her positive feedback. We are encouraged that QXvZ found the paper well-written and easy to follow, recognized novelty of the main idea, and soundness of the experimental design. We address reviewer comments below and will incorporate all feedback in the final ve... | Summary: This paper proposes a new method for high-precision depth prediction on devices with low-precision arithmetic. The authors introduce an innovative technique that represents high dynamic range depth as two low dynamic range components of a 2D Hilbert curve. This approach enables depth maps with higher bit preci... | Rebuttal 1:
Rebuttal: We thank the reviewer RnCh for his/her thoughtful feedback. We are encouraged that RnCh found our claims supported by theoretical analysis and empirical results. We address reviewer comments below and will incorporate all feedback in the final version.
- **“The evaluation metrics used are standard... | Summary: The paper presents a method for achieving high-precision depth prediction on low-precision devices by representing depth as two components of a 2D Hilbert curve. A full-precision DNN is trained to predict these components, and a post-processing step reconstructs high-precision depth from low-precision predicti... | Rebuttal 1:
Rebuttal: We thank the reviewer GYWs for his/her positive feedback. We are glad GYWs has found our idea innovative and recognized that we provided substantial evidence to validate our claims. We are encouraged that GYWs found our approach novel, with high practical value and broad application prospects. We ... | Summary: This paper focuses on high-precision depth estimation on low-precision devices. It leverages a 2D Hilbert Curves for better representation of high dynamic range depth. Extensive experimental results demonstrate the superiority of the proposed method on both accuracy and computational overhead.
Claims And Evid... | Rebuttal 1:
Rebuttal: We thank the reviewer aLW4 for his/her positive feedback. We are encouraged that aLW4 found the paper well-argued and well-structured, recognized clarity of the main idea, effectiveness of the proposed method and soundness of the experimental analysis. We address reviewer comments below and will i... | null | null | null | null | null | null |
MIPT: Multilevel Informed Prompt Tuning for Robust Molecular Property Prediction | Accept (poster) | Summary: The paper introduces Multilevel Informed Prompt Tuning (MIPT), a framework that enhances pretrained Graph Neural Networks for molecular property prediction. MIPT significantly outperforms existing methods, achieving higher ROC-AUC scores while reducing the number of trainable parameters. Key contributions incl... | Rebuttal 1:
Rebuttal: Dear reviewer dDZn:
Thank you for your valuable and constructive comments. We have revised the paper according to your suggestions.
## Experimental Designs Or Analyses
**1. Why choose LoRA**: To learn the features of both node-level and graph-level, we used multi-level fine-tuning, but in orde... | Summary: This manuscript introduces a novel Multilevel Informed Prompt Tuning (MIPT) framework designed to enhance pre-trained molecular encoders for molecular property prediction tasks. The key contributions include a multi-level prompt learning network and a noise penalty mechanism. The proposed prompt learning netwo... | Rebuttal 1:
Rebuttal: Dear reviewer Cxne:
Thank you for your valuable and constructive comments. We have revised the paper according to your suggestions.
**W1: LoRA, as a parameter-efficient fine-tuning method, has been widely adopted in the LLM domain. The manuscript should more clearly clarify LoRA's unique advant... | Summary: The paper introduces a novel framework called Multilevel Informed Prompt Tuning (MIPT) aimed at enhancing the performance of pretrained GNNs in molecular property prediction tasks. MIPT employs a lightweight multilevel prompt learning module to capture task-specific knowledge at both node and graph levels, whi... | Rebuttal 1:
Rebuttal: Dear reviewer kVKq:
Thank you for your valuable and constructive comments. We have revised the paper according to your suggestions.
## Weakness
**W1: In this paper, only the benchmark of classification tasks is studied, and it is necessary to further increase the generality of other types of ta... | Summary: This paper addresses the challenge of prompt tuning for pretrained models in molecular property prediction. It introduces a multi-level prompt learning module to enhance task adaptation and a noise penalty mechanism to improve robustness, adaptability, and efficiency. Extensive experimental evaluations demonst... | Rebuttal 1:
Rebuttal: Dear reviewer Swha:
Thanks for your positive review of our paper and for your thoughtful comments.
**For W1 model versatility.**
- Thank you for your positive feedback on our approach. We appreciate your recognition of the potential our MIPT framework for other graph-level applications.
Bel... | null | null | null | null | null | null |
Learning Cascade Ranking as One Network | Accept (poster) | Summary: The paper introduces LCRON (Learning Cascade Ranking as One Network), a novel end-to-end training framework for multi-stage ranking systems. LCRON formulates cascade ranking as a unified network with a new surrogate loss that aligns all stages with the overall top-$k$ selection objective. In particular, it de... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful review.
For the questions:
1) Yes, the abstract intends to state that L_single helps reduce the gap between $P_{CS}$ and $\hat{P_{CS}}$. In lines 576-588 of the appendix, we explained the conditions for the gap to be reduced to 0 (i.e., the equation in ... | Summary: This paper proposes LCRON (Learning Cascade Ranking as One Network), a new method for optimizing cascading sorting systems. LCRON implements end-to-end training through two agent loss functions (Le2e and Lsingle) to ensure that the goals of each stage are consistent with the overall goals of the system and enh... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed and insightful review.
For weaknesses 1 & 2:
To the best of our knowledge, we have already compared LCRON with existing multi-stage optimization methods. **To further validate the effectiveness of solely using differentiable sorting techniques (i.e., aligni... | Summary: The paper addresses two key challenges in cascade ranking:
(i) the misalignment of training objectives across different stages and
(ii) the discrepancy between training and test environments caused by multi-stage ranking and filtering.
To overcome these issues, the authors propose a novel loss function compri... | Rebuttal 1:
Rebuttal: Thanks very much for the detailed and insightful review.
For the weaknesses:
1) We adopted a two-stage setup (retrieval + ranking) because two-stage cascading represents the most classic form of cascade ranking, as seen in previous works like FS-LTR. From a practical perspective, to the best of... | Summary: The paper "Learning Cascade Ranking as One Network" introduces LCRON, a novel approach for training cascade ranking systems in an end-to-end manner. Traditional cascade ranking architectures suffer from misalignment between training objectives across different stages and discrepancies between training and test... | Rebuttal 1:
Rebuttal: Thanks very much for the detailed and insightful review.
For the questions:
1) In cascade ranking systems, **we can often explicitly define the ground-truth, thus optimizing the end-to-end recall directly maximizes selection efficiency. So we treat it as the golden metric**. Other intermediate m... | null | null | null | null | null | null |
HEAP: Hyper Extended A-PDHG Operator for Constrained High-dim PDEs | Accept (poster) | Summary: This paper focuses on solving high-dimensional time dependent PDE with constraints. Traditional method suffers from curse of dimensionality. The proposed method effectively solves the problem by combining quadratic programing (QP) with NeuralODE. The approach can be summarized as:
1. Reformulating the PDE with... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and suggestions. Here are our responses to your questions:
> Q1: It would strengthen the effectiveness of HEAP if some other constraints were demonstrated
A1: The additional experiment results are reported in the rebuttal supplementary material (RSM), avail... | Summary: The paper introduces HEAP (Hyper Extended Adaptive PDHG), a new neural operator designed to solve constrained high-dimensional PDEs, where solutions must meet additional constraints beyond the governing equations. HEAP learns the evolution of PDE parameters and formulates this process as a quadratic programmin... | Rebuttal 1:
Rebuttal: Thank you for the insightful question. The additional experiment results are reported in the rebuttal supplementary material (RSM), available at <https://anonymous.4open.science/r/HEAP/RSM.pdf>. Regarding your question, we provide the following explanations:
> Q1: What does constraint mean here, ... | Summary: This work provides a principled way of handling constraints in the framework of Control-based solution operator (CSO) for learning PDE solution operators under constraints, called HEAP. In the original CSO, the evolution of the network parameter $\theta$ is governed by a neural network $V$ called the neural co... | Rebuttal 1:
Rebuttal: Thank you for the insightful questions and suggestions. The additional experiment results are reported in the rebuttal supplementary material (RSM), available at <https://anonymous.4open.science/r/HEAP/RSM.pdf>. Here we provide brief responses to your questions point by point:
> Q1: Computationa... | Summary: The paper introduces a novel method called HEAP, designed to solve high-dimensional PDEs that include additional constraints. The PDE solution is approximated with the evolution of the neural network parameters, which is formulated as a quadratic programming problem. To solve the QP efficiently, the method unr... | Rebuttal 1:
Rebuttal: Thanks for your suggestions. The additional experiment results and visualizations are reported in the rebuttal supplementary material (RSM), available at <https://anonymous.4open.science/r/HEAP/RSM.pdf>, and will be added to the draft once allowed. Here we provide responses to your questions point... | null | null | null | null | null | null |
Contour Integration Underlies Human-Like Vision | Accept (poster) | Summary: The authors systematically dissected where and why models struggle with contour integration by designing an experiment that tested object recognition under various levels of object fragmentation. It was found that humans exhibited an integration bias – a preference towards recognizing objects made up of direct... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review. We have addressed your comments individually below and point to an external link for additional figures: https://drive.google.com/drive/folders/1M_nUONfTXLmUZCHHL0PfIlIaEhpu0vLo?usp=drive_link
> Long-range interactions in humans being normally associated to... | Summary: The work investigates the difference between the human ability to generalise object recognition and DNNs. The study builds on experiments that test the ability to recognise objects even in the presence of fragmentation, particularly by contour integration, and the ability of DNNs to perform the same task. The ... | Rebuttal 1:
Rebuttal: Thanks for your review. We are happy to hear that you found our experiments meaningful, and the paper well-written and very interesting. We reply point-by-point to your comments below and point to an external link for additional figures: https://drive.google.com/drive/folders/1M_nUONfTXLmUZCHHL0Pf... | Summary: This paper conducts a nuanced analysis of the extent to which vision models are human-like by conducting an experiment involving categorization of degraded images, where those images are reduced to lines or to fragments that are either points or line segments. Humans are able to recognize the images with fragm... | Rebuttal 1:
Rebuttal: Thank you for your review. We are glad you find our experiments justified and carefully conducted, and that you found the findings interesting. We respond to each of your comments below point-by-point.
> It is not clear whether there are actionable insights for improving models based on our resul... | Summary: The paper presents evidence suggesting that contour integration—a fundamental feature of human vision—remains largely absent in artificial vision models. To demonstrate this, the authors tested human performance on contour integration tasks and evaluated over 1,000 computational models to identify trends in ma... | Rebuttal 1:
Rebuttal: Thank you for your favorable review. We are happy to read that you found the paper insightful and our experiments sound, and that you see how it helps guide future research. We address your comments point by point below, and point to an external link for additional figures: https://drive.google.co... | null | null | null | null | null | null |
From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications | Accept (poster) | Summary: The authors deal with the task of studying low-rank compressions within LLM approaches studying the low-rankness of the weight matrices of the LLM.
Claims And Evidence: The authors discuss the low-rankness of the matrices within the LLM model. The illustrate this numerically, which is nicely shown in Figure 1... | Rebuttal 1:
Rebuttal: We would like to thank you for your time to review our work. Next, we will try to address the weakness pointed by you one-by-one as follows:
**1. Discussion of Sparsity in Section 2.4 and typoes:** Thank you for identifying this mistake. In Section 2.4, we mean low-rank representation and we prom... | Summary: This paper proposes that repeated gradient alignment on the leading Hessian directions gradually drives large transformer models toward low-rank weight configurations. The authors formalize this tendency as a “rank collapse” that can be exploited both for compressing pretrained networks and for selectively fin... | Rebuttal 1:
Rebuttal: We would first like to thank you for the time to review our work. We would now address your weakness point by point as follows:
**1. Computational overhead of SVD on large matrices:** Thank you for raising this point. We would like to highlight that WeLore-COMP is a *one-shot data-agnostic* comp... | Summary: This paper studies the emergence of low-rank structures in Large Language Models (LLMs) through gradient subspace stabilization, revealing that as training progresses, gradients increasingly align with dominant Hessian eigenspaces, driving weight matrices toward low-rank factorization. The authors support this... | Rebuttal 1:
Rebuttal: We would first like to thank you for the time to review our work. We would first like to thank you for finding our work to provide extensive experiments to establish robustness and have practical significance. We would now address your weakness point by point as follows:
**1. Computational overh... | Summary: This paper investigates the low-rank property of LLM weights. The authors identify that low-rank properties vary systematically across components (q/k/v/o/mlp1/mlp2/out) and network depth. Based on this observation, they develop: 1) WeLore-COMP for non-uniform compression across different layers, and 2) WeLore... | Rebuttal 1:
Rebuttal: We would first like to thank you for the time to review our work. We greatly appreciate that you have found our theoretical analysis novel and experimental section comprehensive while identifying the unique proposition of WeLore as a cohesive method to handle compression and fine-tuning in a unifi... | null | null | null | null | null | null |
MathConstruct: Challenging LLM Reasoning with Constructive Proofs | Accept (poster) | Summary: This paper introduces MathConstruct, a novel mathematical benchmark designed to evaluate LLMs' reasoning in constructive proofs from high-school competition problems. The authors curated a dataset of 127 challenging problems from various sources and converted them into a unified format with symbolic parameters... | Rebuttal 1:
Rebuttal: We thank reviewer 6y3x for their review. We are delighted that they recognize MathConstruct as a valuable and novel contribution. We also appreciate their feedback on the clarity of our paper and will incorporate the suggested clarifications. Below, we address their additional questions:
**Q.1. C... | Summary: The paper presents MathConstruct, a new benchmark to test LLMs on constructive mathematical proofs with a symbolic verifier for correctness of each problem. Unlike traditional math benchmarks that focus on fixed numerical answer problems, MathConstruct introduces 127 modified olympiad-level math problems, whe... | Rebuttal 1:
Rebuttal: We thank reviewer S8Ey for their review. We appreciate that they found MathConstruct to be an interesting benchmark, and our experimental design detailed. Below, we address their other questions:
**Q.1. Does robust accuracy suitably measure the correctness of problem solving for a given question?... | Summary: This paper proposes MathConstruct, a new benchmark for mathematical reasoning based on constructive problems from math competitions. These problems are highly interesting, yet benchmarks generally avoid them due to the non unique answers. This paper contributes a suite of problems taken from past along with an... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments, and are happy to read that they find our work novel and relevant to the community, and our framework easy to work with. We address any remaining concerns below.
**Q.1. Can any parts of the pipeline be automated? What challenges did the authors ... | null | null | null | null | null | null | null | null |
Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM’s Reasoning Capability | Accept (poster) | Summary: The paper introduces the concept of critical tokens in mathematical reasoning tasks, which are pivotal points in incorrect reasoning trajectories that significantly influence the final outcome. The authors propose a novel framework for identifying these tokens through contrastive estimation and they further in... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful and detailed comments. Below, we address each concern and hope that our responses sufficiently clarify your questions.
**Weakness**
**W1. Critical Token Estimation**
In our approach, the distribution for critic tokens is approximated using a model traine... | Summary: In this paper, the authors start from the observation that the existence of critical tokens will influence the model performance and propose a contrastive estimation method to identify the critical tokens. At last, the authors proposed the cDPO to improve the model performance.
Claims And Evidence: Yes
Metho... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive feedback and your willingness to engage in further discussions with us. We have responded to each of the issues you raised below and have carefully addressed all your concerns.
**Weakness**
**W1. Baseline results**
Thank you for pointing out this import... | Summary: This paper introduces the concept of critical tokens, which are tokens that significantly influence the reasoning trajectories, leading to incorrect outcomes. They propose to use rollout algorithm to identify critical tokens, then study the difference between critical tokens and wrong tokens. They further prop... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive review. We sincerely appreciate your recognition of our work's novelty and contributions. Your detailed comments are very helpful; we respond to each of your concerns below.
**Concerns:**
**C1. Comparison Between Contrastive Estimation (CE) and the ... | null | null | null | null | null | null | null | null |
BSemiFL: Semi-supervised Federated Learning via a Bayesian Approach | Accept (poster) | Summary: 1. This paper proposes BSemiFL, a federated semi-supervised learning framework. BSemiFL theoretically demonstrates why solely relying on either the global model or the local model for labeling local data is suboptimal.
2. The authors employ a Bayesian approach to evaluate the proximity of local and global mode... | Rebuttal 1:
Rebuttal: Thanks a lot for the reviewing.
```
Q1.FedDB[3] also employs Bayesian in FSSL. FedDB explicitly and mitigates class prior bias from imbalanced data. BSemiFL does not address internal class bias. Additionally, BSemiFL’s analysis is confined to its aggregation strategy and lacks a comparative evalu... | Summary: This paper focus on the semi-supervised scenarios in Federated Learning. This paper delves deeply into the performance dominance and limitations of the global and local models for relabeling the local data from both theoretical and empirical perspectives. Then, they propose a novel method which re-labels the l... | Rebuttal 1:
Rebuttal: Thank you very much for your reviewing and valuable suggestions.
```
Q1：The details of the designed ensemble strategies in Figure 5(a) are not specified. It seems that these methods are designed by this paper itself. However, the details are not clear. For example, what’s the ,meaning of the majo... | Summary: This paper aims to solve the problem in Semi-supervised Federated Learning where the local data labels are absent in clients. They first theoretically and empirically demonstrate that the limitations and benefits of local model and the global model for relabeling the local data. They propose a new method which... | Rebuttal 1:
Rebuttal: Thank you very much for your reviewing and valuable suggestions.
```
Q1：more recent advanced SSL methods [1] can considered.
[1] BEM: Balanced and Entropy-Based Mix for Long-Tailed Semi-Supervised Learning. CVPR 2024.
```
BEM [1] mainly proposes a novel hybrid method for rebalancing the class dist... | Summary: This study focuses on the semi-supervised learning paradigm within Federated Learning (FL), focused on re-label technology. Theoretical and empirical results demonstrate the local model has higher relabel accuracy on local data. Furthermore, this paper propose a Bayesian approach to re-label the local data by... | Rebuttal 1:
Rebuttal: Thank you very much for your reviewing and valuable suggestions.
```
Q1: How to get the p^(xi,k) in 5.1. It is better to use a consistent notation.
```
$p^(x_i,k)=p^(x_i,y_i=k)$ represents the joint probability distribution of the sample $x_i$ and $y_i$, i.e., the probability that the sample takes... | null | null | null | null | null | null |
Quadratic Upper Bound for Boosting Robustness | Accept (poster) | Summary: The paper presents a new adversarial training scheme based on a simple quadratic upper bound (QUB) to the standard adversarial loss, aimed at improving robustness in the context of Fast (single-step) Adversarial Training (FAT).
The authors demonstrate that, when applied on various adversarial training schemes ... | Rebuttal 1:
Rebuttal: ### 1. On the Scope and Effectiveness of QUB across FAT Methods
We appreciate the reviewer’s careful analysis and valuable feedback. As the reviewer pointed out, QUB improves robustness in several FAT methods, but the improvement is limited in strong methods (e.g., FGSM-PGI) and even decreases in ... | Summary: This work demonstrates the convexity of the cross entropy loss and derives its upper bound. Additionally, it applies this to fast adversarial robust training to enhance its adversarial robustness across multiple baselines.
Claims And Evidence: The author presents adequate theoretical evidence to support the m... | Rebuttal 1:
Rebuttal: ### 1. Clarifying Motivation
We appreciate the reviewer’s comment regarding potential conflict between the stated motivation and the proposed method. We acknowledge that the terms “overly robust” and “excessively robust,” used in different parts of the paper, were not clearly differentiated.
In th... | Summary: This paper proposes a novel adversarial training method called Quadratic Upper Bound (QUB), defined as follows:
$$ \mathcal{L}_{\text{QUB}} = \mathcal{L}(f(x)) + (f(x + \delta) - f(x))^T \nabla_f \mathcal{L}(f(x)) + \frac{1}{4} \| f(x + \delta) - f(x) \|_2^2. $$
By incorporating the QUB loss into existing adve... | Rebuttal 1:
Rebuttal: ### 1. On whether QUB effectively mitigates catastrophic overfitting
We appreciate the concern regarding catastrophic overfitting. As clarified in the main text, our goal is not to directly prevent catastrophic overfitting, but to propose a practical and lightweight strategy that enhances stabili... | Summary: This paper provides a new theoretical upper bound of the adversarial training loss and proposes a method to improve the existing fast adversarial training. Specifically, the paper focuses on the problem of catastrophic overfitting, or the degraded robustness after fast adversarial training. To overcome this pr... | Rebuttal 1:
Rebuttal: ### 1. On the practical tightness and overestimation behavior of the QUB loss
We appreciate the reviewer’s emphasis on the importance of validating QUB as a practical upper bound. To evaluate the practical tightness of the proposed QUB loss, we compared QUB and AT loss during training. For most ex... | null | null | null | null | null | null |
Are Large Language Models Ready for Multi-Turn Tabular Data Analysis? | Accept (poster) | Summary: The authors create a synthetic conversational dataset with dialogues about the data tables. The authors scrape the tables from Kaggle. To generate the conversation, the authors organize a multi-agent multi-turn conversation where each agent is an LLM instance prompted to play a specific role.The authors then p... | Rebuttal 1:
Rebuttal: **Additional Evidence in Anonymous Link: https://anonymous.4open.science/r/additional_materials-E646**. We will use `B.X` to index evidence in the following rebuttal:
**C1: The name Decision Company is not clear, specifically, why “decision”?"**
**A1:** The name "DECISION COMPANY" refers to the ... | Summary: This paper introduces CoTA, a benchmark to evaluate the effectiveness of LLMs in multi-turn conversational tabular data analysis scenarios. The authors' motivation is to address the lack of realistic, quantitative evaluation datasets by creating conversational data through an innovative multi-agent sandbox env... | Rebuttal 1:
Rebuttal: **Paper Reference in: https://anonymous.4open.science/r/additional_materials-E646**.
**C1: Comparison with Spider 2.0**
**A1:** In summary, our benchmark differs from Spider 2.0 in several important aspects:
- We focus on **conversational multi-turn** interactions for Python code, while Spider ... | Summary: # Summary
This paper constructs a novel benchmark for the task of "conversational tabular data analysis." The benchmark is constructed using a complex process of interacting agents, aided with human annotation, starting from a set of 5 "data sources" and 18 "topics". This ultimately yields a benchmark of appr... | Rebuttal 1:
Rebuttal: **Additional Evidence & Paper Reference in: https://anonymous.4open.science/r/additional_materials-E646**. We will use `B.X` to index evidence in the following rebuttal:
**C1: Why the annotation is complex and annotators are also authors?**
**A1:**
Because the task that we are researching is com... | Summary: This paper proposed a benchmark, namely COTA, and a multi-agent environment named Decision Company to evaluate the performance of LLMs in the task of Tabular generation. The paper used the proposed benchmark and multi-agent environment to evaluate the performance of 8 LLMs in the task of tabular generation.
... | Rebuttal 1:
Rebuttal: **Concern 1:Client Persona Generation**
**Ans:** Thank you for this valuable feedback. While LLMs were utilized in persona generation, we implemented a rigorous multi-stage validation process specifically to address potential discrepancies between LLM-generated content and real human behavior. Ou... | null | null | null | null | null | null |
Double Machine Learning for Causal Inference under Shared-State Interference | Accept (poster) | Summary: This paper unifies the set of problems in causal inference with interference where the outcomes of individuals depend on others' treatment assignment only through an observed shared state. The paper assumes the units arrive sequentially and models this shared-state problem using the Markov chain. The paper the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We will incorporate your suggestions, including providing coverage rates in Appendix F, in our updated manuscript. In the coverage rate plots, our consistent variance estimator approaches the target coverage rate as $T$ grows, while the coverage... | Summary: This paper addresses causal inference under unit interference in systems like markets and recommendation platforms. To model inter-individual interference without strong assumptions, the authors introduce shared-state variables, assuming individual outcomes depend on others only through these shared states. Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We will incorporate this feedback in our updated manuscript.
* **Application-based simulations.** Exploration of more complex and real-world scenarios (using either simulated or real data) would be a valuable direction for future work. Our focus in this p... | Summary: This paper introduces a double machine learning (DML) estimator for sequentially collected samples where dependencies follow a Markovian structure through a shared-state variable $H_t$.
Claims And Evidence: 1. The paper has a strong theorical foundation.
2. Empirical simulations are lacking of specific expla... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, as well as their comments about the soundness of theoretical results and strength of foundation for the work. We will incorporate your comments into our work. We clarify several points and answer questions below.
* **Data generating process details.** We p... | Summary: This paper studies causal inference in the presence of *shared-state interference*, a common structure in real-world systems such as online marketplaces and recommender platforms, where outcomes of individuals are influenced by a low-dimensional global variable (e.g., price, inventory, recommendations). The au... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback! We also thank you for noting the expressivity of our formality, relevance to practical settings and gap in research on causal inference in algorithmic systems and markets. We respond to each of your questions below:
* **Estimating $m$ in Practice**: We not... | null | null | null | null | null | null |
Non-invasive electromyographic speech neuroprosthesis: a geometric perspective | Reject | Summary: The authors demonstrate a system that translates silently articulated speech into both text and audio. The method collects electromyogram (EMG) signals from multiple articulatory sites on the face and neck during alaryngeal speech and uses GRU with CTC loss to perform sequence-2-sequence decoding. Experiments ... | Rebuttal 1:
Rebuttal: **Response to comment a)**
We thank the reviewer for raising this point. Reference [1] presents a high-performance BCI (brain-computer interfaces) system that records neural activity from the motor cortex at single-neuron resolution. Their approach, involving CNNs for feature extraction and RNNs f... | Summary: The paper introduces a method for converting EMG signal into phoneme sequences without requiring audible speech. The core idea is to use CTC loss and inference, which obviates the need for explicit alignment between the input EMG signals and the output phoneme sequences. The proposed method leverages the funct... | Rebuttal 1:
Rebuttal: **Response to claims and evidence**
*Comment 1)* We thank the reviewer for their comment and the opportunity to clarify. It is important to note that the CTC loss and the SPD matrix representation in our pipeline serve fundamentally different purposes. The use of CTC enables us to demonstrate th... | Summary: This paper introduces a non-invasive EMG decoder that can produce text or audio from silent speech, without EMG responses from any audible speech. The authors found an efficient sparse decomposition of the responses by analyzing their geometry, and GRUs operating on this manifold significantly improved word er... | Rebuttal 1:
Rebuttal: **Response to claims and evidence / Methods and evaluation criteria**
*Comment 1*
Our speech style conversion module follows the implementation provided in [7]. We trained the models using the publicly available code released by [7]. In the revised version, we will move the content currently in A... | null | null | null | null | null | null | null | null |
BDC-CLIP: Brownian Distance Covariance for Adapting CLIP to Action Recognition | Accept (poster) | Summary: In this work, the authors propose an adapter-based action recognition method built on top of CLIP visual and text encoders. To better capture local details, they propose to utilize all visual patch tokens and word tokens and employ Brownian Distance Covariance (BDC) as a similarity metric between video and tex... | Rebuttal 1:
Rebuttal: Dear Reviewer sjZc,
We sincerely thank you for your constructive and insightful comments, particularly your positive feedback & decision.
> ### Q1: The paper lacks evidence demonstrating that BDC enables modeling of complex dependencies in the video-language embedding space.
Thanks for this con... | Summary: This paper proposes BDC-CLIP, a framework that introduces Brownian Distance Covariance (BDC) to address the limitations of current CLIP-based video models. BDC-CLIP can leverage all the visual and textual embeddings and construct non-linear relations for vision-language modeling. BDC-CLIP achieves state-of-the... | Rebuttal 1:
Rebuttal: Dear Reviewer LE28,
We sincerely thank you for providing constructive and insightful comments. In particular, we appreciate your positive comments including **"The motivation is clear and intuitive"** as well as **"The achieved performance is great on a range of benchmarks and experimental setti... | Summary: This paper proposes BDC-CLIP, a novel framework for video-language alignment based on Brownian Distance Covariance (BDC). Unlike cosine similarity, BDC can capture both linear and nonlinear correlations. BDC-CLIP leverages all visual and textual tokens to model both linear and nonlinear relationships in the mu... | Rebuttal 1:
Rebuttal: Dear Reviewer oB1H,
We sincerely thank you for your constructive and insightful comments, especially your positive feedback that **"The paper is well-written and easy to understand"** and **"The proposed method achieves state-of-the-art results on multiple datasets. "**
> ### Q1: However, I ... | Summary: This paper introduces BDC-CLIP, a framework designed to adapt CLIP for video action recognition by using Brownian Distance Covariance (BDC). The authors claim that traditional methods, relying on cosine similarity on global tokens, lack the capacity to capture complex spatio-temporal relations in video data. T... | Rebuttal 1:
Rebuttal: Dear Reviewer GB1s,
We sincerely thank you for providing constructive and insightful comments. Especially, we are grateful for your positive feedback including **"The *novel* integration of BDC into the CLIP framework "**, and **"Comprehensive evaluation compared with previous methods and ablatio... | null | null | null | null | null | null |
Falcon: Fast Visuomotor Policies via Partial Denoising | Accept (poster) | Summary: The paper presents Falcon, an innovative approach that accelerates diffusion-based visuomotor policies without sacrificing their performance. Conventional diffusion policies rely on multiple denoising steps, which can hinder real-time decision-making. Falcon addresses this issue by exploiting the sequential de... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed and thoughtful feedback. We appreciate your recognition of Falcon’s novelty and practical motivation, and we are grateful for your constructive suggestions on deployment, which helped us strengthen the final version of our work.
**Real-World Valida... | Summary: This paper presents Falcon, Fast visuomotor policies via partial denoising. This approach improves diffusion policies by accelerating action generation while preserving the multimodal generation capability. Accelerations are mainly provided by using partial denoised actions to reduce denoising steps. Falcon is... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful suggestion and for the generous score. We deeply appreciate your recognition of our work, and your feedback has helped us identify opportunities to clarify and strengthen our contributions.
**On the Relationship Between $\epsilon$ and $\delta$**
... | Summary: This paper introduces Falcon, a method that accelerates the diffusion process by denoising partially noisy actions at each step using a one-step adaptive mechanism. Extensive experiments validate the speed improvement of the Falcon method on robot datasets.
## Update after rebuttal
I'm satisfied with the auth... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback. We appreciate your recognition of Falcon’s motivation and your suggestions for strengthening the comparison and evaluation. Below, we address each concern in turn.
**On the Effectiveness of the Threshold Mechanism (DDIM... | Summary: The authors propose a method to speed up inference with diffusion policies using a scheme where the denoising chain for action sequences is initialized to a partially noised sequence predicted from a previous timestep. The proposed method is supposed to preserve the multimodality of the diffusion policy while ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and detailed comments. We appreciate the constructive feedback and address the key concern below.
**Clarifying the Motivation and Positioning of Our Method**
Our contribution is not to replace distillation or ODE solvers, but to offer a practica... | null | null | null | null | null | null |
Autoencoder-Based Hybrid Replay for Class-Incremental Learning | Accept (poster) | Summary: In this paper, the authors proposed a method named hybrid autoencoder (HAE) and a strategy named autoencoder-based hybrid replay (AHR). For HAE, it is an autoencoder learnt with charged particle system energy minimization (CPSEM) equations and repulsive force algorithm (RFA). This autoencoder is used for both ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback. We have carefully considered the points raised and we offer the following responses and clarifications:
### On the Theoretical Clarity
- **Regarding O(cte)**:
The notation "O(cte)" was intended as shorthand for "constant time complexity" (O(1)).... | Summary: The paper proposes an Autoencoder-Based Hybrid Replay (AHR) strategy for class-incremental learning (CIL), addressing catastrophic forgetting (CF) and task confusion (TC) while reducing memory complexity. The core innovation is a Hybrid Autoencoder (HAE) that compresses exemplars into a latent space using a re... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thorough review and insightful comments. We are particularly grateful for the recognition of AHR's strengths. Below, we clarify and address issues pointed out by the reviewer:
### On Applicability to Task-Free CIL
We'd like to clarify that **Figure 2** i... | Summary: The paper tackles the problem of class incremental learning, where the model sees a sequence of tasks of different classes, and needs to adapt to them sequentially while minimizing catastrophic forgetting and task confusion. During testing, the model does not have access to the task ID.
To solve this problem... | Rebuttal 1:
Rebuttal: We are impressed by the reviewer's high-quality feedback, which reflects a deep understanding of the field and engagement with our work. We particularly appreciate the fairness of their critique. In response, we offer the following clarifications:
### On the Choice of Not Changing Class Centroid ... | null | null | null | null | null | null | null | null |
Cover learning for large-scale topology representation | Accept (poster) | Summary: This paper introduces "cover learning," an innovative unsupervised learning method designed to represent the large-scale topological structure of geometric datasets. It extends and addresses limitations present in traditional Topological Data Analysis (TDA) methods, specifically those relying on geometric comp... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback!
- W1: This will be addressed in the ablation study/sensitivity analysis we will perform.
Please see "Main comment A" in the response to reviewer mPj7 for what we plan to include, as well as preliminary findings.
- W2:
We will include this.
In our experienc... | Summary: This paper proposes a novel algorithm for learning subset cover of a dataset with respect to its geometric and topological properties. The authors develop a gradient optimization procedure for learning the fuzzy cover of a dataset with required properties; fuzzy cover induces a simplicial filtration (by grade ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback!
- About time and memory complexity:
On Table 4 in the Appendix, we report the times it took to run our experiments.
Separately to this, we will include a complexity analysis in the paper.
- About practical applications: please see "Main comment C", at the ... | Summary: The paper aims to generate topologically faithful simplicial complexes for geometric datasets by reducing the problem to cover learning. By formally defining a set of three goals for cover learning, extending to the space of fuzzy covers (“softening” the inclusion of an element in a subset, which allows them t... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback!
- About missing references: We will include them in the paper and comment on similarities and differences.
Here are the main ideas we took from the papers (please correct us if we have misinterpreted something):
- (Huguet et al. 2023)(Brugnone et al. 20... | Summary: This paper focuses on learning a representation of the large-scale structure of geometric datasets, and specifically this is achieved by learning the cover of the geometric datasets. The performance of existing methods such as the 1D Mapper or Differentiable Mapper is sensitive to the choice to hyper parameter... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback!
- About most of the results being visualizations: Three out of five experiments concern visualizations.
We just want to emphasize the fact that the two other experiments concern topological inference, with experiment A being a quite thorough comparison with... | null | null | null | null | null | null |
High-Fidelity Simultaneous Speech-To-Speech Translation | Accept (poster) | Summary: The paper introduces Hibiki, a decoder-only model for simultaneous speech-to-speech (S2ST) and speech-to-text (S2TT) translation. Unlike offline approaches, Hibiki translates speech in real-time using a multistream language model that synchronously generates text and audio tokens. The model leverages contextua... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback.
# Updates in the revised version of the paper
We first inform the reviewer that we will update the reported results of Hibiki-M in [Table 2](https://hibiki-s2st.github.io/e.png) after fixing an issue that further improves performance.
Followi... | Summary: This paper introduces Hibiki, a decoder only model for simultaneous speech-to-speech/text translation. Hibiki adapts the architecture of a full-duplex dialogue model Moshi to simultaneous translation by modeling source speech as user input and target speech as agent response. To train Hibiki, the authors synth... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback.
# Updates in the revised version of the paper
We first inform the reviewer that we will update the reported results of Hibiki-M in [Table 2](https://hibiki-s2st.github.io/e.png) after fixing an issue that further improves performance.
Followi... | Summary: This paper introduces a model named Hibiki for real-time speech-to-speech translation. Hibiki employs a multi-stream architecture to synchronously process source and target speech, and generates both text and audio through multi-task learning. Trained with a weakly supervised method, Hibiki demonstrates SOTA p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback.
# Updates in the revised version of the paper
We first inform the reviewer that we will update the reported results of Hibiki-M in [Table 2](https://hibiki-s2st.github.io/e.png) after fixing an issue that further improves performance.
Followi... | Summary: This paper proposes a state-of-the-art speech-to-speech translation system called Hibiki. This is a chunk-based decoder-only model based on Mimi codec, and a number of techniques (alignment-related, synthetic data creation, classifier-free guidance, etc.) are introduced to achieve state-of-the-art performance ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback.
# Updates in the revised version of the paper
We first inform the reviewer that we will update the reported results of Hibiki-M in [Table 2](https://hibiki-s2st.github.io/e.png) after fixing an issue that further improves performance.
Followi... | null | null | null | null | null | null |
Calibrated Value-Aware Model Learning with Probabilistic Environment Models | Accept (poster) | Summary: The paper investigates value-aware model learning (VAML), particularly examining the MuZero loss and Iterative VAML (IterVAML) within a unified framework termed (m, b)-VAML. The authors present theoretical insights, showing that standard (m, b)-VAML losses are generally uncalibrated surrogates when applied to ... | Rebuttal 1:
Rebuttal: Thank you for your review. We are thankful you took the time to engage with our paper! For your concerns on the number of environments and experiments, please refer to our reply to reviewer v9zD. Graphs can be found here https://drive.google.com/file/d/178cVcy05grmQ-dZCFu1p8ixIItgghoxG/view?usp=sh... | Summary: The paper analytically investigates *value aware model learning* (VAML) and puts it into relation with the MuZero loss, by defining a generalizing (m,b)-VAML loss. The authors show that both approaches, that is, (1,0) and (1,1)-VAML, are *uncalibrated* because averaging over samples introduces a variance term,... | Rebuttal 1:
Rebuttal: Thank you for your kind and thorough review. We are grateful you took the time to engage very thoroughly with our paper! For your concerns on the number of environments and experiments, please refer to our reply to reviewer v9zD. These extended experiments also addresses concerns about significanc... | Summary: This paper examines a systematic issue in value-aware model learning (VAML) for reinforcement learning (RL).
Core Idea of VAML:
Unlike standard model learning, which maximizes log-likelihood, VAML optimizes for a model that results in zero value-function error. In other words, even if the model does not accur... | Rebuttal 1:
Rebuttal: Thank you for your kind and thorough review. We are grateful for your comments. For your concerns on the number of environments, please refer to our reply to reviewer v9zD. Graphs can be found here https://drive.google.com/file/d/178cVcy05grmQ-dZCFu1p8ixIItgghoxG/view?usp=sharing
Bias-variance tr... | Summary: Value aware model learning losses penalizes if the model's value function prediction goes wrong. This works theoretically investigates the losses and shows that generally these losses are uncalibrated surrogate losses. They devise corrective measure for the losses. They provide experimental results on DMC cont... | Rebuttal 1:
Rebuttal: Thank you for your review. For your concerns on the amount of environments, please refer to our reply to reviewer v9zD. Additional graphs can be found here https://drive.google.com/file/d/178cVcy05grmQ-dZCFu1p8ixIItgghoxG/view?usp=sharing
Regarding your concern about novelty: while we agree that ... | Summary: The paper studies the family of value-aware model learning losses in model-based reinforcement learning, including MuZero loss. By theoretical analysis, it shows these losses are essentially uncalibrated surrogate losses. Then it proposes corrections for the losses. Experiments are conducted to show the correc... | Rebuttal 1:
Rebuttal: Thank you for your review. We are using this reply as a general reply since all reviewers raised similar concerns and this review is the first one that appears on open review. As ICML does not allow an updated manuscript, but all reviewers are mostly concerned about empirical questions, we provide... | null | null | null | null |
CodeIO: Condensing Reasoning Patterns via Code Input-Output Prediction | Accept (oral) | Summary: The paper reports on work on generating training data for reasoning tasks from code. The method generates training examples from code by using a LLM to generate the query, input-output pairs with their reasoning chains, and input predictions from the output together with their reasoning chains. The data is use... | Rebuttal 1:
Rebuttal: Thank you for the time and effort you spent reviewing our paper, and for recognizing our contributions. Below are our responses:
# Q1
> "Universal gains/effectiveness" is a strong claim and is not defined in the paper.
Thanks for this comment, we will change this statement containing “universal” ... | Summary: The paper introduces a training paradigm where models are taught to predict input–output pairs from code and accompanying test cases. The key idea is to leverage the structured nature of code to instill reasoning skills while preserving procedural rigor. In practice, the authors transform raw code into executa... | Rebuttal 1:
Rebuttal: Thank you for the time and effort you spent reviewing our paper, and for recognizing our contributions. Below are our responses:
# Q1
> Diverse reasoning patterns may not be fully captured by code & Compare to other methods that tackle abstract, non-linear reasoning
Thanks for this comment. We ag... | Summary: The paper introduces CODEI/O, a novel approach designed to enhance the reasoning capabilities of large language models by leveraging code input-output prediction. The key idea is to transform code into a format where models predict inputs or outputs given a function, while reasoning in natural language using C... | Rebuttal 1:
Rebuttal: Thanks for the time and effort you spent reviewing our paper, and for recognizing our contributions. Below are our responses:
# Q1
> Evidence to show code integrates diverse reasoning patterns
We conduct a case study on the CodeI/O dataset, and witness typical examples with certain reasoning pat... | Summary: In this work, the authors develop CodeI/O and CodeI/O++ to improve the reasoning capabilities of Large Language Models. The proposed CodeI/O approach trains models to predict code inputs and outputs in natural language. Evaluation on a variety of benchmark datasets and base models show that CodeI/O improves re... | Rebuttal 1:
Rebuttal: Thank you for the time and effort you spent reviewing our paper, and for recognizing our contributions. Below, we have listed our responses to your questions and comments.
# Q1
> The claim "CodeI/O and CodeI/O++ exhibit universal effectiveness …" is not exactly true given that the proposed method ... | null | null | null | null | null | null |
Reducing Confounding Bias without Data Splitting for Causal Inference via Optimal Transport | Accept (poster) | Summary: This paper focuses on the causal inference task, specifically in the binary and continuous treatment settings. The authors argue that data sparsity can hinder covariate distribution alignment across different treatment groups, leading to biased outcome predictions. Instead, they push all conditional marginals ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the valuable comments. We will revise the submission according to the comments and responses.
**Q1**
A visualization of the balance results is missing.
**A1**
Thank you for the valuable comments.
We have further conducted experiments to visualize the embeddings be... | Summary: This paper proposes an effective algorithm for estimating treatment effects while reducing confounding bias, applicable to both binary and continuous treatments. It employs optimal transport methods to utilize all available samples for estimating confounding bias, thereby mitigating bias and avoiding the decre... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the valuable comments. We will revise the submission according to the comments and responses.
**Q1**
The performance advantage of the proposed method diminishes in binary cases and limited continuous treatment cases.
**A1**
This observation is reasonable.
Compared ... | Summary: This paper proposes a novel method for causal effect estimation based on optimal transport. The method reduces the confounding bias without data splitting, which is different from the existing methods that partition training into multiple groups according to treatments. Theoretical and empirical results are pr... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the valuable comments. We will revise the submission according to the comments and responses.
**Q1**
Regarding the effect estimation error of the continuous treatment setting.
**A1**
We analyze the effect estimation error of the continuous treatment setting below.... | Summary: This paper extends CFRNet to use all samples for each treatment when computing loss functions. Thus, it fits the continuous treatment setting better as it does not suffer from the sample splitting problem. However, this is at the cost of modeling propensity scores and density estimation for $q(x)$ and $q_t(x)$... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the valuable comments. We will revise the submission according to the comments and responses.
**Q1**
The time and space complexity for computing the Wasserstein distance.
**A1**
1) In practice, to avoid heavy computation, we consider a set $\widehat{\mathcal{T}}$ i... | null | null | null | null | null | null |
Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training | Accept (poster) | Summary: The paper introduces a hierarchical GNN for all-atom glycan modeling supported by a multi-scale pre-training strategy.
Claims And Evidence: The paper is well-written and easy to understand.
Methods And Evaluation Criteria: Yes
Theoretical Claims: There are no theoretical claims in the paper.
Experimental D... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments! We respond to your concerns as below:
>**Q1: Why is the pre-training method less helpful to RGCN than to GlycanAA?**
We deem that **the less benefit of pre-training to RGCN mainly owes to its lower model capacity**. Compared to GlycanAA that models both monosa... | Summary: The paper introduces GlycanAA, a novel framework for All-Atom Glycan Modeling using hierarchical message passing and self-supervised pretraining. It models glycans as heterogeneous graphs where atom nodes represent local structures and monosaccharide nodes represent the global backbone structure. GlycanAA empl... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and constructive suggestions! We respond to your questions as below:
>**Q1: Can the model handle more complex glycans with diverse glycosidic linkages?**
We announce that **the proposed GlycanAA model can handle any glycan no matter how complex its structure is*... | Summary: This paper proposes a hierarchical graph model for atom-level glycan modeling. It employs self-supervised learning to enhance the model's capability. The self-supervised learning framework uses multi-scale mask prediction as its task. Subsequently, the pre-trained model is utilized for downstream tasks. The hi... | Rebuttal 1:
Rebuttal: Appreciate your insightful comments and golden suggestions! We respond as below:
>**Q1: An ablation study focusing on the proposed hierarchical graph during the pre-training stage is recommended.**
This suggestion is great. By removing the atom-level modeling part, the obtained model variant of ... | null | null | null | null | null | null | null | null |
Linear Contextual Bandits With Interference | Accept (poster) | Summary: This paper is the first to study contextual bandits with interference. The authors propose a linear contextual bandit framework and introduce an algorithm called LinCB to address the regret minimization/estimation problem under this framework. Experimental results based on MovienLens dataset further demonstrat... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful questions and the time you spent reviewing our paper. We really appreciate your insights and are happy to discuss any further ideas or questions you may have.
**Answer to W1**:
Thank you for your insightful perspective on the literature of Dubey et al. (2020). We would... | Summary: This paper investigates the problem of interference in linear contextual bandits, where the actions taken for one unit influence the rewards of others. This paper leverages an adjacency matrix to model the interference structure and proposes three online algorithms LinEGWI, LinUCBWI, and LinTSWI. The authors e... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful questions and the time you spent reviewing our paper. We really appreciate your insights and are happy to discuss any further ideas or questions you may have.
**Answer to W1**:
Regarding the assumption that the interference matrix is known, we clarify this in three asp... | Summary: The paper explores the intersection of causal inference and multi-armed bandits, specifically in the setting of multi-agent bandits with interference among agents. According to the authors, this is the first work in the literature to incorporate contextual information (i.e., the covariates of units). Under cer... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful questions and the time you spent reviewing our paper. We really appreciate your insights and are
happy to discuss any further ideas or questions you may have.
**Q1**:
Eq. (1) models the reward of unit $i$ in round $t$ as $R_{ti}=\sum_{j=1}^{N_t} W_{t,ij} f_{tj}+\eta_{... | Summary: The paper introduces a framework to address Linear Contextual Bandits with interference, where the actions of one unit can affect the rewards of others. The authors bridge the gap between causal inference and online decision-making by explicitly modeling interference through a linear structure involving an int... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful questions and the time you spent reviewing our paper. We really appreciate your insights and are
happy to discuss any further ideas or questions you may have.
**Q1**:
In the 2nd paragraph of Sec 7, we briefly mentioned how we could proceed to jointly estimate $(\Phi, ... | null | null | null | null | null | null |
Importance Sampling for Nonlinear Models | Accept (poster) | Summary: The paper introduces a framework to generalize norm-based and leverage-score-based importance sampling from linear models to nonlinear settings. It uses adjoint operator of a nonlinear map, to do so. The authors demonstrate that their sampling methods provide guarantees analogous to linear models, enabling app... | Rebuttal 1:
Rebuttal: Dear Reviewer JXa1, we sincerely appreciate the time you devoted to reviewing our paper and your comments. We aim to address your comments and questions in detail below.
### Additional Sampling Methods in Classification
Thank you for the suggestion. While adding
more sampling methods would ideally... | Summary: The paper proposed a sampling method for important data by extend the norm and leverage scores in linear models to nonlinear models and reduce the computational complexity.
Claims And Evidence: The proposed methods for important data points in nonlinear models offer several advantages, such as reduced computa... | Rebuttal 1:
Rebuttal: Dear Reviewer Z4FF, we sincerely appreciate the time you took to review our work. To the best of our ability, we aim to address your comments and questions in detail below.
### Experiments with High-dimensional Data
Thank you for your observations and feedback. As mentioned at the outset in the i... | Summary: This paper introduces a novel family of distributions that extends leverage score distributions—widely used in subset selection for linear models—to nonlinear models. The key component of this construction is a newly defined nonlinear adjoint operator, which satisfies the identity:
$L(\theta) = \|\|\hat{F}^{*... | Rebuttal 1:
Rebuttal: Dear Reviewer Nx6D, we are grateful for the time and effort you devoted to reviewing our paper. We sincerely hope to address your comments and questions in detail below.
### Scope of Theoretical Guarantees
1. Thank you for your observation. While the examples in the paper (single-index models, ReL... | null | null | null | null | null | null | null | null |
Multi-band Frequency Reconstruction for Neural Psychoacoustic Coding | Accept (poster) | Summary: This paper proposes multi-band frequency spectral residual vector quantization (MBS-RVQ) for quantizing latent speech across different frequency bands. Additionally, the results demonstrate the performance of zero-shot text-to-speech models using the proposed Neural Audio Codec.
## Update after rebuttal
Whil... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and the effort you’ve put into helping us improve our presentation.
**Novelty (It appears that there are some misapprehension):**
Our quantizer fundamentally differs from plain RVQ by employing a multi-band split directly at the latent level, guided by psychoacou... | Summary: MUFFIN is an improved RVQ-based neural audio codec (NAC) using a multi-band spectral split for each RVQ sub-layer, to better disentangle different frequency bands into separate RVQ sub-layer codebooks ("psychoacoustically guided"). This enables improved bitrate allocation based on psychoacoustic studies, which... | Rebuttal 1:
Rebuttal: We appreciate your dedication to carefully scrutinize our work and it means a lot to us.
**Disabling MBS:**
We agree that conducting an ablation study by disabling “MBS” is important to better demonstrate its contribution to the reconstruction performance. Part of this analysis has already presen... | Summary: The paper introduces MUFFIN, a neural psychoacoustic codec leveraging Multi-Band Spectral Residual Vector Quantization (MBS-RVQ) and a modified snake activation function. By decomposing latent representations into psychoacoustically motivated frequency bands, MUFFIN optimizes bitrate allocation and achieves st... | Rebuttal 1:
Rebuttal: We thank you for your constructive comments and thoughtful concerns, which help to improve the impact of this work and spark further discussion.
**Using larger audio set:**
We agree with the valid concern regarding the size of the environmental audio dataset, and we welcome further discussion on ... | null | null | null | null | null | null | null | null |
Towards a Formal Theory of Representational Compositionality | Accept (poster) | Summary: This paper introduces a notion of compositionality grounded in algorithmic complexity. The authors propose to treat compositionality employing Kolmogorov complexity related to representations and to a discrete language that is used to make the conversion. The contribution rests mainly in bridging how such a me... | Rebuttal 1:
Rebuttal: Thank you for the constructive review.
**Limitation:** $Z$ **is continuous,** $W$ **is discrete**
While continuous values are problematic for Kolmogorov complexity, this may not be a significant limiting factor in practice: for instance, tokenization methods for continuous data (e.g., VQ-VAE) of... | Summary: The paper builds a more rigorous version of compositional generalization, compared to the ones proposed by linguists. It claims to be the first to do so, although this may be debatable. This seems to be the first serious attempt based on kolmogorov complexity and is more agnostic of the learning model achitect... | Rebuttal 1:
Rebuttal: Thank you for the constructive review.
**Tempering claims on novelty and highlighting limitations**
Reviewer JsGE made a similar comment—in retrospect, we agree. While we believe that existing definitions of compositionality suffer from pitfalls that ours addresses, it is unfair to claim that ou... | Summary: This paper argues that a quantitative measure of compositionality, beyond the traditional colloquial definition, is needed for a more precise understanding of the concept. The authors propose a measure of representational compositionality based on optimal compression using Kolmogorov complexity. Specifically, ... | Rebuttal 1:
Rebuttal: Thank you for the constructive review.
**Q1 / Should we aim to measure the compositionality of** $W$**,** $f$**, or** $Z$**?**
In our definition $C(Z)$, we are interested in the compositionality of representation $Z$ where no $W$ or $f$ are provided. We believe that our definition does in fact m... | Summary: This submission frames compositionality as a quantitative measure of how compressible a representation is into a specific family of probabilistic models. This quantitative measure of compositionality is tested in three settings: One in which the generative model of the data is known and specific parameters can... | Rebuttal 1:
Rebuttal: Thank you for the constructive review.
**Tempering our claims**
Reviewer q4Av made a similar comment—in retrospect, we agree. While we believe that existing definitions of compositionality suffer from significant pitfalls which our definition addresses, it is unfair to claim that ours is the fir... | null | null | null | null | null | null |
Hierarchical Reinforcement Learning with Targeted Causal Interventions | Accept (poster) | Summary: This paper considers Hierarchical Reinforcement Learning (HRL) by leveraging causal discovery to improve training efficiency in long-horizon tasks with sparse rewards. In particular, the subgoal structure is modeled as a causal graph and an algorithm to learn this hierarchy is introduced. Instead of random sub... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and address the concerns under the following headings.
___
## 1- Reviewer: The evaluation seems reasonable, but still...
As the reviewer mentioned, grid-world environments are widely used in HRL research, as they capture key challenges such as subg... | Summary: This paper tackles the long-horizon RL tasks with hierarchical abstractions. Specifically, the authors propose Hierarcahical RL via Causality (HRC) which enables the agents to prioritize some causally impactful subgoals over the others. Among the HRC framework, the authors also develop a new subgoal-based caus... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and address the concerns under the following headings.
___
## 1- Reviewer: The ablation study...
We already conducted several ablation studies (Figure 6) to evaluate our approach. To assess the impact of our targeted strategy, we compared three HRC... | Summary: The paper presents a novel approach to Hierarchical Reinforcement Learning by leveraging causal discovery to identify hierarchical structures among subgoals. The key contribution is a causal discovery algorithm that learns the subgoal structure, which is then used to guide interventions during exploration. Thi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and address the concerns under the following headings.
___
## 1- Reviewer: In particular, I’m worried about two assumptions...
Regarding controllability, our current framework assumes discretized subgoals, which is a reasonable assumption in doma... | null | null | null | null | null | null | null | null |
Textural or Textual: How Vision-Language Models Read Text in Images | Accept (poster) | Summary: This paper systematically analyzes how the encoder-only vision-language models (i.e., CLIP) perceives the textual and semantic information. The paper uses ID and linear probe to measure the representation complexity and semantic perception ability of vision-language models. The analysis suggests that at earlie... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments. We appreciate the focus on the underlying assumptions behind our interpretation, and we have carefully examined all concerns related to experimental validity and inference logic. We address each point below, and will incorporate clarifications, resu... | Summary: This paper investigates images with overlaid text, and how vision-language models process them. The paper analyzes representations throughout model layers using Intrinsic Dimension as a measure of complexity. It finds that early layers primarily encode textures while the last one encodes semantics. Through the... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and encouraging review. We're glad the main idea came through clearly: we aim to understand how vision-language models handle overlaid text, and how representational insights can guide robustness improvements. Your suggestions on Figure 5 and the evaluation setup are e... | Summary: This paper investigates Typographic attacks in vision language models. They investigate whether these models encode textual semantics through representation complexity, and identify the mechanisms by which text disrupts visual understanding.
To decouple orthography from semantics, they introduce the ToT datas... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and open review. This paper is primarily an **interpretability-driven study**: our goal is to understand how VLMs represent and process text in images across layers. Rather than proposing a new defense method, our core contribution lies in using Intrinsic Dimension (I... | Summary: This paper explores how vision-language models (e.g., CLIP) process text in images via the ToT (Textural or Textual) dataset, showing that early layers rely on visual texture while semantic understanding emerges in the final blocks. Using Intrinsic Dimension (ID) analysis, the paper reveals changing representa... | Rebuttal 1:
Rebuttal: We thank the reviewer for their close reading and thoughtful comments. We would like to clarify that this paper is primarily an **interpretability study** that analyzes how vision-language models process text in images, using Intrinsic Dimension (ID) as a lens to reveal the transition from textura... | null | null | null | null | null | null |
From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining | Accept (poster) | Summary: This paper introduces MELP, a novel multi-modal ECG foundation model that leverages hierarchical supervision at the token, beat, and rhythm levels from clinical text to improve ECG representation learning.
Experimental results on multiple public ECG datasets demonstrate that MELP outperforms existing self-su... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful evaluation and for recognizing the strength of our comprehensive experiments and the novelty of our multi-level supervision design. **We also updated the manuscript accordingly.**
**[D.1] Clarification of Token-level Pretraining (Q1)**
Thanks for acknowle... | Summary: This paper proposes a multimodal self-supervised pretraining method for paired electrocardiograms (ECGs) and text. This method, MELP, is unique in its use of multi-scale representation learning and supervision by breaking down an ECG signal into hierarchical levels of the full rhythm view, the smaller beat vie... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful evaluation and for recognizing the novelty of our multi-scale ECG-language pretraining approach. We also appreciate your positive remarks on the clarity of our writing and the thoroughness of our ablation studies. Below, we address your concerns r... | Summary: This study proposes MELP (Multi-scale ECG-Language Pretraining), which introduces an innovative multi-scale supervision mechanism in the field of ECG pretraining. By integrating cross-modal alignment at the token, beat, and rhythm levels, MELP effectively enhances the feature learning capability of ECG signals... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review and for acknowledging the novelty of our multi-level supervision and the strength of our experiments. We also appreciate your constructive feedback on the token-level design, which we address below. **All responses are updated in the revised manuscri... | Summary: The authors propose Multi-scale ECG-Language Pretraining (MELP), which is a two-step process: First is the cardiology language pretraining step, which pretrains a text encoder using cardiology-focused corpus to maximize the language model’s utility for cardiology. Second step is the multimodal pretraining step... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful and detailed review. **All responses have been updated in our revised manuscript**.
**[A.1] Support for Multi-level Motivation**
Thanks for your suggestion. From prior work, we have found several supports for diagnosing ECG by multi-level observations:
- **Token-leve... | null | null | null | null | null | null |
To Steer or Not to Steer? Mechanistic Error Reduction with Abstention for Language Models | Accept (poster) | Summary: This paper introduces Mechanistic Error Reduction with Abstention (MERA), a framework for conditional activation LM activation steering that addresses a fundamental challenge in the steering literature: that interventions can often hurt overall performance and are often applied unnecessarily. Unlike traditiona... | Rebuttal 1:
Rebuttal: Thank you for all these important remarks. We’re encouraged that you found our work a timely and relevant contribution to the steering literature and that our focus on steering for error reduction is a differentiator/ strength! We’ve addressed all of your remarks below. Please let us know of any r... | Summary: The authors a new steering technique (MERA). MERA formulates steering as an error reduction problem. It first trains a linear probe to determine the linear direction which is most effective for reducing the error. It then adaptively selects a steering multiplier alpha based on how far the prediction is from th... | Rebuttal 1:
Rebuttal: Thank you for the very detail-oriented and helpful review! We’re excited that you believe our work has benefits for transfer learning and personalisation and that our mathematical framework may become a building block in future work. We have addressed all of your points below:
**1) Evidence for o... | Summary: The paper proposes a new latent space steering methodology for LLMs. The basic setup is that we prompt an LLM with a question from a finite-class classification task, and we let it generate an open-ended response. We can decode the model's prediction from its open-ended generation by either
- **last token**: ... | Rebuttal 1:
Rebuttal: Thank you for all the time taking to provide a very detail-oriented and helpful review! We’re glad to hear that you found our claims straightforward, clear + verifiable, our optimisation framing is a great way to move the fields towards more principled foundations and our experimental designs are ... | Summary: Current steering methods for LM error mitigation use fixed intervention strengths, which has the risk of under-/oversteering. The paper introduces MERA, which does adaptive activation steering guided by linear error probes; the intervention thresholds (α) is calibrated via Hoeffding’s inequality. The framework... | Rebuttal 1:
Rebuttal: Thank you for the constructive and informative review! We have addressed your four key points below:
**1) Hoeffing inequality — Clarifying Our Selection Procedure.**
Our selection procedure ensures we do **not** choose any $\alpha \in (0,1)$ unless it **statistically significantly** improves per... | null | null | null | null | null | null |
LEMoN: Label Error Detection using Multimodal Neighbors | Accept (poster) | Summary: LEMON is a method designed to identify mislabeled image-caption pairs in large vision-language datasets, which often contain noisy data scraped from the web. Unlike previous approaches that rely solely on image-caption embedding similarity for filtering, LEMON leverages multimodal neighborhood information in t... | Rebuttal 1:
Rebuttal: Thank you for the insightful review and constructive feedback!
> I noticed that the most recent dataset used was published in 2020. Is the method still effective on datasets published in the last two years? Do mislabeled data still exist in these recent datasets?
We clarify that we evaluated our... | Summary: This paper tries to apply a neighbor-based noisy sample detection method to a multimodal dataset (image-text pairs dataset) with the help of a pre-trained vision-language model. The authors also provide theoretical proof to show that their method has better noise detection capability than random detection. Exp... | Rebuttal 1:
Rebuttal: Thank you for the insightful review and constructive feedback!
> In appendix A.2, authors only present the visualization results for classification tasks, which is a much easier case than tasks involving image captioning, where the text can be natural language and have more diverse space on J(x).... | Summary: The paper presents LEMoN, a method to detect label errors in paired image-text data by using a pretrained CLIP model. Given a dataset of image-text pairs, LEMoN constructs a score $f(x,y)$ which is a weighted combination of CLIP score of $(x,y)$ and two nearest neighbor based intra-modal scores. The intuition ... | Rebuttal 1:
Rebuttal: Thank you for the insightful review and constructive feedback!
> Empirical performance on realistic noise. Downstream performance on CC3M and Datacomp is almost the same as Clip Similarity baseline.
First, we would like to emphasize that we evaluate our method against the baselines on several o... | Summary: This paper presents LEMoN, a method for detecting label errors in image-text pair datasets. The authors define a scoring function in the CLIP embedding space that combines the pairwise image-text distance with distances to nearest neighbors in both the image and text modalities. Specifically, the score integra... | Rebuttal 1:
Rebuttal: Thank you for the insightful review and constructive feedback!
> For instance, BLIP-based filtering methods may not be prohibitively expensive, as they can be fine-tuned on a domain-specific dataset (e.g., movie or biomedical domains) with relatively modest computational costs. Such fine-tuning c... | null | null | null | null | null | null |
Sharp Generalization for Nonparametric Regression by Over-Parameterized Neural Networks: A Distribution-Free Analysis in Spherical Covariate | Accept (spotlight poster) | Summary: This paper studies the generalization bounds of two-layer neural networks under the neural tangent kernel (NTK) regime.
Using the critical radius as an error measure, this paper establishes distribution-free generalization bounds of the network, which recovers the optimal bounds derived in the previous literat... | Rebuttal 1:
Rebuttal: We appreciate the review and the suggestions in this review. The raised issues are addressed below. In the following text, the line numbers are for the revised paper.
Regarding the weakness, we emphasize that the two-layer neural network studied in this paper still achieves the sharp risk bound o... | Summary: This paper addresses the generalization capabilities of over-parameterized two-layer neural networks (NNs) trained by gradient descent (GD) with early stopping for nonparametric regression. The authors establish a sharp generalization bound for the nonparametric regression risk. This result is distribution-fre... | Rebuttal 1:
Rebuttal: We appreciate the review and the suggestions in this review. The raised issues are addressed below. In the following text, the line numbers are for the revised paper.
(1) “Can the results be extended to deeper neural networks? If so, what additional challenges arise in the analysis?”
Yes, this w... | Summary: This manuscript contributes to the generalization analysis of overparameterized ReLU neural networks (with one hidden layer) in the context of nonparametric regression tasks. The training data $\{(\overrightarrow{x_i}, y_i)_{i=1}^n\}$ is assumed to be such that $\overrightarrow{x_i}$ are drawn from a unit sph... | Rebuttal 1:
Rebuttal: We appreciate the review and the suggestions in this review. The raised issues are addressed below.
(1) "Can the authors provide any other examples as extensions of Theorem 5.1? For example, if the target function belongs to an RKHS ball induced by the Gaussian kernel, what should be the converge... | Summary: The authors study risk rate convergence of the infinite-width NTK for two-layer neural networks with ReLU activations trained with gradient descent and early stopping. The authors present theoretical results that relax some assumptions made in prior work: specifically it is common for results to be derived on ... | Rebuttal 1:
Rebuttal: We appreciate the review and the suggestions in this review. The raised issues are addressed below. In the following text, the line numbers are for the revised paper.
**(1)"... It seems like the authors here are trading off one somewhat restrictive assumption (uniform spherical data) for another ... | null | null | null | null | null | null |
Screener: Self-supervised Pathology Segmentation Model for 3D Medical Images | Reject | Summary: In this paper, an unsupervised visual anomaly detection algorithm is proposed, which the authors describe as a segmentation algorithm, although I disagree with this characterization. The method exploits the inherent rarity of pathological patterns compared to healthy ones. Two different self-supervised learnin... | Rebuttal 1:
Rebuttal: Dear Reviewer *pFWf*, thank you for your thorough review and valuable feedback. We appreciate your thoughtful questions and have carefully addressed each point below.
**Terminology: anomaly detection vs. segmentation**
We agree that terminology in this field can vary. In our work we use **"anoma... | Summary: The authors introduce Screener, a self-supervised 3D pathology segmentation model that formulates the task as an unsupervised visual anomaly segmentation (UVAS) problem. It utilizes self-supervised feature learning and a masking-invariant condition model within a density-based UVAS framework. Trained on 30,000... | Rebuttal 1:
Rebuttal: Dear Reviewer *hWnx*, thank you for taking the time to review our submission and for providing thoughtful and valuable feedback. Your suggestions regarding our evaluation design were especially valuable, and we have done our best to accomplish them, as well as address your other concerns.
**Inclu... | Summary: This paper proposed Screener, a self-supervised anomaly segmentation framework for volumetric CT images.
The Screener was built upon dense self-supervised learning and a density-based anomaly segmentation framework. Specifically, it utilizes dense pixel-wise self-supervised learning (i.e., VICReg) to pretrain... | Rebuttal 1:
Rebuttal: Dear Reviewer *X8PP*, thank you for your careful review and constructive feedback. We appreciate your acknowledgment of our contributions and have carefully addressed your suggestions below.
**Inclusion of recent baselines**
We recognize the value of benchmarking our approach against recent stat... | Summary: The paper presents Screener, a framework based on unsupervised visual anomaly segmentation (UVAS) for 3D medical scans. The proposed model aims to reduce the dependency on ground truth (GT) annotations. It is trained on a large dataset of 30K CT scans and evaluated on 1.8K scans, covering a variety of patholog... | Rebuttal 1:
Rebuttal: Dear Reviewer *pobP*, thank you for your thoughtful review of our submission and for the time you dedicated to providing such detailed and relevant feedback. Your critical comments on our experimental design were particularly valuable, and we have done our best to address them thoroughly in the re... | null | null | null | null | null | null |
Revisiting Neural Networks for Few-Shot Learning: A Zero-Cost NAS Perspective | Accept (poster) | Summary: This paper proposes an entropy-based expressivity metric, namely Few-shot Neural Architecture Search (IBFS), for training-free neural architecture search (training-free NAS), which uses Jacobian eigenvalues at initialization to estimate model performance. The authors claim that the motivation for such formulat... | Rebuttal 1:
Rebuttal: Thank you for the helpful and insightful review, which is very helpful for us to further improve this paper. Next, we will answer your questions one by one, and we hope this will improve your acceptance of the paper.
**Q1**: Concern about Theorem 4.1. **A1**: Many thanks for your comments! We reg... | Summary: The paper proposes a novel framework called IBFS (Information Bottleneck-driven Few-shot Neural Architecture Search) for few-shot learning (FSL) tasks. IBFS leverages the Information Bottleneck (IB) theory to rank and select neural architectures without requiring any training, significantly reducing search cos... | Rebuttal 1:
Rebuttal: Thank you for the helpful and insightful review, which is very helpful for us to further improve this paper. Next, we will answer your questions one by one, and we hope this will improve your acceptance of the paper.
**Q1**: Sensitivity.
**A1**: Many thanks for your comments! We will provide som... | Summary: This paper mainly considers the case that NAS is applied in few-shot learning scenarios, where previous works mainly search for the optimal architecture from scratch or borrow the architecture from other tasks. The paper presents a novel framework called IBFS (Information Bottleneck-driven Few-shot Neural Arch... | Rebuttal 1:
Rebuttal: Thank you for the helpful and insightful review, which is very helpful for us to further improve this paper. Next, we will answer your questions one by one, and we hope this will improve your acceptance of the paper.
**Q1**: Concern about optimal architecture for few-shot learning tasks.
**A1**:... | Summary: The paper introduces IBFS (Information Bottleneck-driven Few-shot Neural Architecture Search), a novel framework designed to efficiently select neural architectures for few-shot learning (FSL) without requiring any training. Traditional NAS approaches either search architectures from scratch—resulting in high ... | Rebuttal 1:
Rebuttal: Thank you for the helpful and insightful review, which is very helpful for us to further improve this paper. Next, we will answer your questions one by one, and we hope this will improve your acceptance of the paper.
**Q1**: Concern about MAML.
**A1**: Many thanks for your comments! Our paper fo... | null | null | null | null | null | null |
Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoE | Reject | Summary: This paper proposes Jakiro, a method that boosts the performance of speculative decoding for Large Language Model (LLM) inference acceleration. Speculative decoding employs a smaller, faster "draft" model to predict upcoming tokens, which a larger "target" model then verifies. Jakiro introduces two primary i... | Rebuttal 1:
Rebuttal: > C1: This claim attributes speedup to "minimizing the risk of errors during the inference process". What errors are the authors talking about?
Thank you for pointing out the ambiguity in our phrasing. The "risk of errors" refers to the probability that a token generated by the draft model is rej... | Summary: This paper presents Jakiro, which utilizes the MoE technique to do two-token-ahead parallel decoding to enhance the diversity of draft model prediction. Upon the framework of EAGLE, Jakiro replaces the MLP layer of EAGLE drafter with an MoE layer consisting of a router and several experts.
The authors also i... | Rebuttal 1:
Rebuttal: > C1: The authors should give a clear definition of the diversity of draft tokens and then quantify it with such a definition.
Thanks for your thoughtful feedback. Building upon our previous response to Reviewer LwQj (W1), as illustrated in the figure, our Jakiro model, even with just two MOE hea... | Summary: This paper proposes Jakiro, which leverages Mixture of Experts (MoE), where independent experts generate diverse predictions, effectively decoupling correlations among candidates. It demonstrates universal improvements across multiple different benchmarks.
Claims And Evidence: Yes, LLM acceleration is a very ... | Rebuttal 1:
Rebuttal: > W1: The authors should test over more advanced settings like flash decoding.
Thank you for the feedback. We first clarify that Flash Decoding (FD) and speculative decoding (SD) operate at different optimization levels but can be effectively combined for greater efficiency.
**Flash Decoding (D... | Summary: The paper claims that Jakiro improves speculative decoding by leveraging the Mixture of Experts (MoE) for dynamic decoupling and introduces a hybrid inference strategy that combines autoregressive decoding with parallel decoding in the last steps. The authors also claim that Jakiro achieves state-of-the-art pe... | Rebuttal 1:
Rebuttal: > W1: The analysis of diversity is missing in the experiments.
To validate that our Jakiro method enhances the diversity of speculative sampling, we conduct a comparative analysis against Eagle2 (with temperature=1). Using a prompt from mt_bench: "Compose an engaging travel blog post about a rece... | null | null | null | null | null | null |
Primphormer: Efficient Graph Transformers with Primal Representations | Accept (poster) | Summary: This paper proposes a novel Graph Transformer model, named Primphormer, which models the self-attention mechanism in the primal space, avoiding costly pair-wise computations and enabling an efficient variant of Graph Transformers. By introducing an additional primal objective loss, Primphormer achieves high ef... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of the innovation of Primphormer and insightful comments. We address your concerns below:
- R4.1: Broader experiments.
> Thank you for mentioning the latest baselines, Graph ViT/MLP-mixer [1], GRIT [2], and GEAET [3]. Following your suggestion, we have included co... | Summary: This paper introduces an efficient graph transformer, called Primphormer, that addresses the quadratic complexity issue of traditional graph transformers by using a primal representation. The authors showed that Primphormer serves as a universal approximator for functions on both sequences and graphs, retaini... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of the novelty of Primphormer. We address your concerns below:
- R3.1: Broader experiments.
> Following your suggestion, we have compared our method with other efficient graph Transformers such as Polynormer and SGFormer, as illustrated in the following table.
>| ... | Summary: This work presents Primphormer, a graph transformer architecture that leverages a primal-dual framework to reformulate the self-attention mechanism for graphs, which has previously been done for self attention for sequences. Unlike previous graph transformers (such as GraphGPS with a global vanilla Transformer... | Rebuttal 1:
Rebuttal: Thank you for your insightful suggestions. We address your concerns below:
- R2.1: The removal impact performance of $f_X$.
> Following your insightful suggestion, we report the performance drop of removing $f_X$ in the following table.
>| | PascalVOC | COCO | Peptides-Func | Pept... | Summary: The paper aims to bypass the scale-restricting quadratic complexity of graph transformers with a primal representation of self-attention. This is accomplished by extending the linear-complexity primal representation of self-attention on sequences presented in [1]. The authors identify the lack of ordering in g... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments and appreciation of this work. We address your concerns below:
- R1.1: The reason for using Sumformer.
> We use Sumformer as a bridge to analyze the approximation. Specifically, we decompose the approximation into two parts: (1) Primphormer to Sumformer and (... | null | null | null | null | null | null |
Machines and Mathematical Mutations: Using GNNs to Characterize Quiver Mutation Classes | Accept (poster) | Summary: In this paper, the authors study the problem of quiver mutation using the GNNs and GNN explanation tool. The author identified that the GNNs trained with naive classification tasks on predicting quiver mutation type are able to learn causal information related to quiver mutation. In particular, it can identify... | Rebuttal 1:
Rebuttal: Thank you for your feedback and insightful questions. We're glad to hear you found the topic interesting, and we hope to answer your questions below:
> Is it possible to evaluate on a graph with a size much larger than training (like 20 or 30), and will the conclusions still hold?
The propertie... | Summary: This work uses GNNs to solve the quiver-mutation-equivalence problem: whether one quiver can be transformed into the other through a sequence of mutations. With explainability techniques, they discovers criteria for quiver of type $\tilde D$. Moreover, GNN need not to be trained.
Claims And Evidence: Yes.
Me... | Rebuttal 1:
Rebuttal: We appreciate your feedback and your suggestions regarding presentation. Below we hope to address your concerns and questions.
> This work is more similar to a experiment report rather than a paper, as no novel algorithm or tasks are proposed, and the result is used to verified existing results r... | Summary: This paper shows that a GNN learns the same substructure to classify the quiver mutation class of a quiver as proposed by a classification theorem in quiver theory in mathematics. The authors train a GNN on quivers of different types $A,D,E,\tilde{A},\tilde{D},\tilde{E}$ and use PGExplainer on this trained mod... | Rebuttal 1:
Rebuttal: We appreciate your insightful review. We are encouraged you think our work demonstrates the promise of machine-guided research, and we are glad you found the paper well-organized. Since multiple reviewers had questions about the experimental design, we have also expanded the discussion in the pape... | Summary: The paper uses GNNs to learn quiver mutation equivalence. The results show that GNNs can not only classify these quiver types, but can also characterize particular mutation classes through their latent representations.
Claims And Evidence: Most claims are supported by evidence including theoretical results, e... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review, and we are glad you found the application to quiver mutation interesting. We hope to address your comments below:
> As admitted in the paper, the gap between train and test set (including the distribution of the number of nodes, and the absence of $\widetilde... | null | null | null | null | null | null |
A Variational Perspective on Generative Protein Fitness Optimization | Accept (poster) | Summary: The paper focus on the problem of protein fitness optimization to find new variants with enhanced fitness, facing challenges such as a vast search space and discrete protein sequences. The introduced Variational Latent Generative Protein Optimization (VLGPO) is a variational framework that enables posterior sa... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing feedback on our manuscript and appreciate the comments. We will also discuss related work that you mentioned.
- **The predictors and the oracle are sourced directly from previous works.**
Indeed, we take this from recent work from ICLR’24 [1]. As mentioned to... | Summary: The paper proposes Variational Latent Generative Protein Optimization (VLGPO), a method for protein sequence optimization by training a VAE combined with a learned flow matching prior over mutations. A fitness predictor is used for guidance, and the method is evaluated on commonly used database lookups includi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their careful evaluation of our manuscript and for the positive assessment of our work. Below, we address each point individually:
- **The authors may be interested in this very recent in silico benchmark, which introduces synthetic test functions that can be u... | Summary: This paper presents a novel protein fitness optimization model called Variational Latent Generative Protein Optimization (VLGPO). VLGPO uses flow-matching to perform fitness optimization in the continuous latent space of the generative model, allowing efficient exploration of the fitness landscape. Guided by f... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their valuable comments and feedback. Below, we address each point individually:
- **Since the evaluation of the methods relies on the predictor, it would be helpful to provide details on the accuracy of $g_{\phi}$ as in-silico oracle.**
Thank you for hig... | Summary: This paper proposes a new in-silico method for generating novel high-fitness protein sequences. It first embeds sequences in a lower-dimensional space via a VAE, then fits a generative model to the embeddings by flow-matching. The sampling is guided by a pre-trained fitness predictor and manifold-constrained g... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the constructive feedback and the insightful comments, which help to improve the quality of the manuscript.
We fully agree with the reviewer that assessing fitness with a computational model is less reliable than direct wet-lab validation. Experimental vali... | null | null | null | null | null | null |
Maximum Update Parametrization and Zero-Shot Hyperparameter Transfer for Fourier Neural Operators | Accept (poster) | Summary: This paper applies the Maximum Update Parametrization (µP) framework to Fourier Neural Operators (FNO), demonstrating that a single set of hyperparameters can effectively work for both large-scale and small-scale FNO models.
Claims And Evidence: While I understand the authors' claims, the significance of this... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful comments and constructive suggestions. Let us respond to your concerns one-by-one below.
**Regarding the significance of our work.**
Our main contributions can be summarized as follows:
* **On the theory side:** We are the first to derive the Maximum Upda... | Summary: This paper introduces μTransfer-FNO, a zero-shot hyperparameter transfer method for Fourier Neural Operators (FNOs). The core idea is to derive a Maximum Update Parametrization (μP) for FNOs that allows hyperparameters tuned on small FNOs to be directly transferred to larger FNOs without additional tuning, eve... | Rebuttal 1:
Rebuttal: Thank you for supporting our paper! We respond to your main questions and concerns below.
**Regarding the scope of PDEs.** Following your and other reviewers' suggestions, we conduct additional experiments on a mixed equation dataset involving Burgers' Equation, Advection Equation, and Reaction-D... | Summary: The authors discuss how Fourier Neural Operators (FNOs), which is a state-of-the-art SciML method, have been used to solve complex PDEs. However, they identify issues with scaling FNO to more intricate PDEs that requires increasing the number of Fourier modes. Increasing the number of Fourier modes increases t... | Rebuttal 1:
Rebuttal: Thank you for supporting our paper! We respond to your main questions and concerns below.
**Regarding motivating the need for larger Fourier modes.** A concrete example is based on Kolmogorov microscales in fluid dynamics: When simulating turbulent flows governed by the Navier-Stokes equations, t... | Summary: This paper introduces $\mu$Transfer-FNO, a zero-shot hyperparameter transfer technique for Fourier Neural Operators (FNOs). Based on the Maximum Update Parametrization (μP) framework, the authors propose a parametrization scheme that enables hyperparameters tuned on smaller FNOs to be transferred directly to m... | Rebuttal 1:
Rebuttal: Thank you for supporting our paper! We respond to your questions and concerns below.
**Regarding the connection to Transformers.** This is an interesting point! The Fourier Integral Operator and Continuum Attention are both nonlocal operator classes, but they have different parametrizations:
- F... | null | null | null | null | null | null |
Censor Dependent Variational Inference | Accept (poster) | Summary: The paper proposes a censor-dependent conditional VAE (CD-VAE), where two variational posteriors—for censored and non-censored events—are inferred given covariates and observed times, instead of the typical single posterior assumed in baseline methods. Further, the paper provides theoretical results to support... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their time and effort in engaging with our work. Your recognition of the importance of variational survival analysis is encouraging. We've added further experimental results and endeavor to clarify a few points that may have been misinterpreted.
## Response to ... | Summary: This paper builds upon prior work (Nagpal et al. 2021a, Apellaniz et al. 2024) that uses the variational distribution $q_\phi (z \mid x, y)$ as a posterior approximation for $p_\theta (z\mid x)$, without accounting for censoring (i.e., $y$ and $\delta$). The authors propose a variational inference method that ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your thoughtful feedback and valuable suggestions. We truly appreciate the recognition of the contribution and your overall support of our work. We have improved our manuscript based on your recommendations. In what follows, we respond to your comments point by point, wi... | Summary: This paper analyzes the current practices to apply variational inference to latent variable models for survival analysis, provides insights into why the naive application of VI may be insufficient, and presents a new VI formulation that can potentially sidestep some of those challenges. The authors also includ... | Rebuttal 1:
Rebuttal: We truly appreciate your thoughtful and constructive feedback and your "willing to accept" recommendation. Your reasoning between what is well-explained and what is unclear is greatly appreciated. The following responses address the points you raised.
# Evaluation: Best metrics, average metrics, m... | null | null | null | null | null | null | null | null |
Universal Approximation Theorem of Networks Activated by Normalization | Reject | Summary: The authors study the approximation power of MLPs with no traditional activation functions but instead only with the *layer norm* between affine layers. They show that this too is a universal approximator. Personally, I find this result interesting as it is something I've wondered about showing my self but ... | Rebuttal 1:
Rebuttal: ### Reply to the reviewer G246
Thanks for your valuable comments and suggestions. We are pleased with your support for our paper.
---
#### **Response to Question 1**
Thanks for figuring out this typo. We indeed want to express "better optimization property" with the words "optimization capaci... | Summary: The authors study universal approximation for networks, where the activation function is replaced by a layer normalization. This result is traced back to the classical universal approximation theorem by Cybenko. However, this step contains an error, as the sigmoid function derived from LN does not act element... | Rebuttal 1:
Rebuttal: ### Reply to the reviewer 1Q2E
Thanks for your valuable comments and suggestions.
The main concern raised by the reviewers is the correctness of our proof. After re-examining our proof, we are confident that there are **no errors** in our reasoning. Here, we attempt to clarify why the reviewer... | Summary: This paper explores the possibility of replacing activation functions with layer normalization, offering a new perspective on the foundational logic of neural networks. It provides corresponding approximation theory, width estimates, and experimental results, including a theoretical proof of the universal appr... | Rebuttal 1:
Rebuttal: ### Reply to the reviewer Guee
Thanks for your valuable comments and suggestions.
---
#### **Response to Weakness 1**
To begin with, we list our contributions below to clarify the novelty of this paper.
1. We are the first to **consider LN (and LS) as activation functions** and provide the *... | null | null | null | null | null | null | null | null |
Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations | Accept (poster) | Summary: This work proposes a new active learning framework for optimizing desirable properties in CRISPR screening, which is a combinatorial problem.
The main algorithmic contribution comes down to an adaptive training method that scales the embedding dimensionality of the predictive model with the size of the trainin... | Rebuttal 1:
Rebuttal: Dear Reviewer PDeb,
We greatly appreciate your thoughtful feedback and questions. Your comments have helped us identify key areas where we could improve the presentation and clarity of our work. We will incorporate the additional results from the experiments in the revised manuscript. Below, we a... | Summary: We can perturb the expression of various genes to achieve a desirable phenotype such as enhanced cell viability. However, given that there are close to 20k known human genes, it is not possible to test every gene combination to identify the optimal combination that leads to the most desirable phenotype. This p... | Rebuttal 1:
Rebuttal: Dear Reviewer kwgr,
We appreciate your supportive feedback and your valuable comments, which have helped us to improve our work further. We will incorporate the results from your suggested experiments in the revised manuscript. We address your questions below. Due to space constraints, we have th... | Summary: This paper presents an active learning framework that efficiently discovers optimal gene pairs by leveraging single-gene perturbation effects and adaptive gene embeddings. The experiments show that the proposed method achieve better performance than baseline methods.
Claims And Evidence: Yes.
Methods And Eva... | Rebuttal 1:
Rebuttal: Dear Reviewer vQva,
Thank you sincerely for taking the time to review our work. We’re encouraged that you found the applications of our method interesting. We appreciate your thoughtful feedback about accessibility for a broader ML audience. We will incorporate a concise, ML-focused framing in th... | null | null | null | null | null | null | null | null |
An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints | Accept (poster) | Summary: The paper considers the online learning problems in the constrained MDP problems. The objective is to maximize the reward while satisfying the constraints, where the system state transits according to an underlying MDP. The problem is formulated as a finite horizon episodic setting with $H$ periods in each epi... | Rebuttal 1:
Rebuttal: We appreciate the valuable comments provided by the reviewer. We address the reviewer's questions as follows.
>**Response to Essential References Not Discussed**
We will explicitly cite and discuss these references in our final revised manuscript, clearly positioning our primal-dual contributions... | Summary: This paper introduces the Optimistic Mirror Descent Primal-Dual (OMDPD) algorithm, a novel approach for online constrained Markov decision processes (CMDPs) with anytime adversarial constraints. Unlike prior methods that assume known safe policies or rely on Slater’s condition, OMDPD achieves optimal regret $\... | Rebuttal 1:
Rebuttal: We appreciate the valuable comments provided by the reviewer. We address the reviewer's questions as follows.
>**Response to Weakness**
>**W1: Removing Slater’s condition** While it is true that in some practical scenarios, a near-feasible solution may exist, it doesn't make any changes to our re... | Summary: The paper studies the online learning problem for episodic constrained Markov decision processes, where the constraint functions are either stochastic or adversarial, and the transition function is unknown. A key technique the authors introduce is a surrogate objective function for the policy optimization step... | Rebuttal 1:
Rebuttal: We appreciate the valuable comments provided by the reviewer. We address the reviewer's questions as follows.
>**C1: Bounded $\mathcal{C}$** In our paper, we assume that $U$ is a 1-strongly convex function. When $U(q)$ is the entropy function, the resulting Bregman divergence is the KL divergence... | null | null | null | null | null | null | null | null |
Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning | Accept (poster) | Summary: This paper studies the problem of multi-player general-sum robust Markov games (RMGs). Via proposing a new robustness measure called fictitious uncertainty set that centers at $(s,a_i)$ instead of $(s,\mathbf a)$, the authors break the curse of multi-agents (i.e., the dependency is $\sum_{i=1}^n \lvert A_i\rve... | Rebuttal 1:
Rebuttal: ### Q1. The idea of fictitious uncertainty set looks interesting, can it be useful somewhere else.
Fictitious uncertainty sets, inspired by behavioral economics, hold significant value in game theory and behavioral economics and have several meaningful applications:
* **Understanding human prefe... | Summary: The paper consider the problem of strategic interactions in uncertain environments; namely, robust Markov games (MGs). Robust Markov games are the multi-agent extension of Markov decision processes. The authors consider MGs where the transition kernel, i.e., the dynamics governing state transition, drift from ... | Rebuttal 1:
Rebuttal: ### 1. "The proposed algorithm is the first to overcome the curse of multiagency in RMGs, irrespective of the uncertainty set types." Is it due to an additional assumption?
Our work breaks the curse of multiagency **through two key innovations: the introduction of a new class of fictitious RMGs an... | Summary: The paper proposes a robust multi-agent reinforcement learning framework based on a new fictitious uncertainty set. It proves the existence of robust Nash equilibria and coarse correlated equilibria then introduce a novel algorithm, Robust-Q-FTRL, which adaptively samples from a nominal generative model and so... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the careful reading of the paper and the insightful and valuable feedback.
### 1. Additional experiments verifying the effectiveness of Robust-Q-FTRL against baseline methods could be beneficial.
Thank you very much for this valuable suggestion! As the reviewe... | Summary: This paper addresses the robustness issue in MARL by proposing a novel approach based on fictitious uncertainty sets. The main contributions are as follows:
1. The authors define a new type of uncertainty set, which incorporates both environmental uncertainty and the behavior of other agents. Then they prove t... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for recognizing and appreciating our contributions, both in terms of the problem formulation and the technical results. This acknowledgment is extremely rewarding!
### 1. How does this work perform in real-world MARL tasks, and are there existing practical applicat... | null | null | null | null | null | null |
A Bayesian Model Selection Criterion for Selecting Pretraining Checkpoints | Accept (poster) | Summary: This paper studies the problem of neural network model selection under the pretrain-then-adapt paradigm. Based on the pretraining data, multiple neural network checkpoints can be obtain roughly corresponding to different local minimums of the network parameters. To select a better choice that adapts well to do... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our work. We are concerned that the major criticisms do not fully justify a “reject” recommendation. Below, we show that the issues raised can be readily resolved with minor clarifications or references, rather than indicating any fundamental flaw.... | Summary: This paper introduces a Bayesian model selection criterion called the downstream free energy, which quantifies the adaptability of pretraining checkpoints for downstream tasks. By measuring the concentration of favorable parameters for the task, this criterion helps predict fine-tuning performance without requ... | Rebuttal 1:
Rebuttal: Thank you for your time in reviewing our paper. We note that the reviewer raised two concerns—(1) the absence of certain references ([1–5]) and (2) the limited dataset scope (CIFAR-100 and mini-ImageNet)—and offered no further questions or objections. You’ll find below our best efforts to address ... | Summary: This paper proposes a new metric, pretraining free energy, which can be used to find a pretraining model checkpoint which is most adaptable for downstream finetuning tasks. The paper is largely theoretical, justifying this metric, although there are two experiments (one in appendix) showing that WBIC, which is... | Rebuttal 1:
Rebuttal: Thank you very much for your careful attention to our paper and thoughtful review. We are glad you think our paper is "clear" and that the "structure of the narrative was good". We will do our best to answer your concerns regarding potential weaknesses below.
> Experimental Designs or Analyses: ... | Summary: This paper introduce a Bayesian model selection criterion, called the downstream free energy, to improve fine-tuning performances. There are both theoretical and empirical results provided.
Claims And Evidence: Yes. Section 5 is about theoretical results, and empirical results are in Section 6.
Methods And E... | Rebuttal 1:
Rebuttal: Thank you for your time in reviewing our paper. We are glad you think our paper is "well-written" and provides a "clear statement of results" supported by both "theoretical and empirical evidence". Below, we address the potential weaknesses you mentioned, and we hope these clarifications will enco... | null | null | null | null | null | null |
Revealing Weaknesses in Text Watermarking Through Self-Information Rewrite Attacks | Accept (poster) | Summary: The paper proposes a new attack against model-watermarking algorithms that involves first identifying tokens in an LLM output that have high self-information, and then passing the output to a paraphraser that changes these tokens. The hypothesis is that these tokens are also the tokens that most likely contain... | Rebuttal 1:
Rebuttal: We are thankful to Reviewer 7XjW for the thorough and detailed feedback, due to space limit, we address the main concerns below:
> Q1: A Pareto plot, the use of \epsilon, Line 1041
A1: We greatly appreciate the reviewer’s suggestion. We will add the Pareto plot, correct the misuse of \epsilon, ... | Summary: The paper aims to erase text watermarks from LLMs by proposing a novel rewrite attack utilizing self information. The proposed SIRA could achieve almost 100% success rate across various watermark algorithms. Specifically, SIRA calcuates self information of every token in the watermarked sequence, where tokens ... | Rebuttal 1:
Rebuttal: We are thankful to the reviewer **P6b2** for the appreciation of our work and the efforts spent to review our paper. We address concerns and questions below:
> **Q1: Text quality slighter lower than GPT paraphrase**
**A1:** We thank the reviewer for their suggestion and will revise the descripti... | Summary: This paper studies how to remove the watermark in text generated by LLMs. It assumes the watermark is injected through the high-entropy (self-information) words. There it first uses an auxiliary mode to compute the self-information for each token in the generated text. Then it masks out the tokens with high se... | Rebuttal 1:
Rebuttal: We are thankful to the reviewer zCjb for the time spent reviewing our paper. Due to the reply character limit, we address the main concerns below and put the reference in anonymous link:
> Q1: No theoretical analysis or direct experimental evidence on the high-entropy assumption
A1: The use of h... | Summary: The paper introduces SIRA, a novel text watermark attack method that leverages the concept of self-information to efficiently and effectively remove watermarks from text generated by large language models. The authors conduct systematic experiments to demonstrate the effectiveness of their approach.
Claims An... | Rebuttal 1:
Rebuttal: We are thankful to the reviewer **RVPR** for the valuable time and effort spent reviewing our paper. We elaborate on the questions raised by the reviewer below:
> **Q1: Clarification regarding claims: SIRA-Tiny method outperforms all previous approaches**
**A1:** We would like to clarify that th... | null | null | null | null | null | null |
MATS: An Audio Language Model under Text-only Supervision | Accept (poster) | Summary: The authors propose to use pre-trained audio-text contrastive models such as CLAP to achieve text only supervision, with a strongly-related noisy text with audio mechanism to introduce robustness.
Claims And Evidence: The authors compare proposed methods to several other audio large language models and show t... | Rebuttal 1:
Rebuttal: ### We sincerely appreciate your time and effort in reviewing our manuscript. Your positive evaluation is highly encouraging. Thank you for your valuable feedback. | Summary: This paper proposes a text-only supervision method that closes the gap between the text embedding space and the audio embedding space via a mechanism called santa.
## Update after rebuttal
I deeply appreciate authors providing additional results. It resolves my other concerns except for this one: "connection ... | Rebuttal 1:
Rebuttal: ### **Q1: The proposed Santa does not directly use the disc_L1 metric.**
1. In our design, we only have access to text-only data during training, making it impractical to directly use the disc_L1 metric to reduce the modality gap. Instead, as shown in Figure 3 of main paper, our Santa achieves ... | Summary: This paper proposes MATS, an audio-language multimodal large language model (LALM) that is trained solely on text data while achieving strong performance on various audio comprehension tasks. Unlike conventional LALMs, which require a large corpus of audio-language pairs, MATS leverages CLAP (Contrastive Langu... | Rebuttal 1:
Rebuttal: ### **W1: Training an ablated system that replaces Santa with mechanisms of prior works (PromptAAC and DRCap)**.
Following your suggestion, we replace Santa with the modality-gap reduction mechanism of PromptAAC and DRCap, referred to MATS-PromptAAC and MATS-DRCap. As shown in Table 1, Santa ach... | null | null | null | null | null | null | null | null |
AutoGFM: Automated Graph Foundation Model with Adaptive Architecture Customization | Accept (oral) | Summary: This paper introduces an automated graph foundation model with adaptive graph neural architecture customization. The authors address the architecture inconsistency problem in graph foundation models. The proposed method consists of graph encoder, architecture customization, and curriculum training. The theoret... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to the reviewer for the detailed comments and insightful questions. We respond to each of the reviewer’s comments point by point as follows.
> 1. "Clarify the hyperparameter tuning process and discuss more on ablation studies."
Thank you for bringi... | Summary: The paper introduces a framework for adapting GNN architectures dynamically to improve generalization in GFMs. Existing graph neural architecture search methods struggle to design architectures for GNN-based GFMs. This paper addresses the issue of architecture inconsistency by identifying an invariant relation... | Rebuttal 1:
Rebuttal: We would like to express our sincere appreciation to the reviewer for providing us with detailed suggestions. We have carefully reviewed each comment and offer the following responses.
> 1. "Can you explain what claims you want to support with the showcase Figure 4?"
Thank you for highlighting t... | Summary: The authors introduce GFA, a framework for graph neural network architecture customization in graph foundation models. The paper addresses the architecture inconsistency problem, which arises when different graph domains and tasks require varying GNN architectures. To tackle this, the authors propose a disenta... | Rebuttal 1:
Rebuttal: We sincerely appreciate the insightful comments provided by the reviewer. We have carefully considered each point raised and would like to respond as follows.
> 1. "The experiment should consider additional way settings beyond the current specific configurations."
Thank you for raising this impo... | Summary: This paper explores automated graph neural architecture search (GNAS) for Graph Foundation Models (GFMs) to overcome the limitations of fixed, hand-designed GNN architectures, which result in suboptimal performance across diverse graph domains and tasks. The authors identify the architecture inconsistency prob... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to the reviewer for providing us with detailed comments and insightful questions. We have carefully considered the reviewer's feedback and would like to address each point as follows.
> 1. "Could you clarify whether the assumptions in the theories ca... | null | null | null | null | null | null |
Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models | Accept (poster) | Summary: This paper proposes a new algorithm for dictionary learning, namely Iterative Codebook Feature Learning (ICFL), which can be optionally augmented with the PCA whitening technique. This technique is then applied to interpret features learned in masked autoencoder models trained on microscopy images of cells. Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive review. We would like to respond below to a few comments made by the reviewer.
> The experiment lacks comparisons between the proposed methods and other existing approaches to interpretability. It is not clear how much practitioners can gain from usin... | Summary: This paper explores the application of dictionary learning (DL) to extract biologically meaningful concepts from large-scale masked autoencoders (MAEs) trained on microscopy images. The authors introduce Iterative Codebook Feature Learning (ICFL), a dictionary learning algorithm adapted from the Matching Pursu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and for raising helpful questions. We would like to respond below to a few comments made by the reviewer.
> Single way of assessing feature interpretation
While linear probing is one important way to evaluate the quality of the features, we disag... | Summary: This paper adapts techniques from the mechanistic interpretability literature to address the problem of discovering what concepts are learned by foundation models trained on microscopy data. Specifically, they develop a new dictionary learning method, which they call ICFL, based orthogonal matching pursuit to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and appreciate their positive feedback on the paper. We were especially pleased to read that the reviewer appreciated our extensive case study. We would like to respond below to a few comments made by the reviewer.
> The only step in the method that I fou... | null | null | null | null | null | null | null | null |
Rethinking the Temperature for Federated Heterogeneous Distillation | Accept (poster) | Summary: This paper highlights the suboptimal temperature calibration issue in existing federated distillation (FD) methods. To address this, the authors propose ReT-FHD, which introduces multi-level elastic temperature to dynamically adjust distillation intensities across different model layers and category-aware glob... | Rebuttal 1:
Rebuttal: We thank reviewer e9EW for the constructive comments: "…dynamically adjust distillation intensities across different model layers...", "…validate the effectiveness of ReT-FHD...". To thoroughly address your concerns, we will answer the questions one by one:
**Questions:**
**Q1: Connection betwee... | Summary: The paper introduces ReT-FHD, a framework for heterogeneous federated knowledge distillation that contributes three core ideas. First, it proposes Multi-level Elastic Temperature to adaptively regulate how much knowledge is distilled at each layer, enhancing cross-architecture consistency. Second, it implement... | Rebuttal 1:
Rebuttal: We thank reviewer TEBy for the constructive comments: "…enhance accuracy under model/data heterogeneity", "…reduces communication costs compared to proxy-based methods", "… represents a novel synthesis of ideas from robust FL, KD, and malicious detection … ", "… address a variety of practical depl... | Summary: The paper introduces ReT-FHD, which aims to improve the efficiency of federated learning by addressing model and data heterogeneity. The paper’s key motivation comes from the common weakness of current federated distillation methods, i.e., suboptimal temperature calibration during knowledge fusion. Therefore, ... | Rebuttal 1:
Rebuttal: We thank reviewer JGAK for the constructive comments: "…reducing communication costs and enhancing security …", "… provides clear experiments …". To thoroughly address your concerns, we will answer the questions one by one:
**Questions:**
**Q1: Differences from existing dynamic temperature knowl... | null | null | null | null | null | null | null | null |
DragSolver: A Multi-Scale Transformer for Real-World Automotive Drag Coefficient Estimation | Accept (poster) | Summary: The authors present DragSolver, a Multi-scale Transformer for processing car point clouds to estimate drag coefficient for automotive designs in real-world applications. To adapt traditional transformer architectures to this new task, the authors propose multiple designs to achieve more trustful results, like ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s insightful and constructive comments. We have conducted additional experiments and analyses based on your suggestions, and these results will be explicitly included in the revised manuscript.
**Important Note:** To clearly demonstrate the thoroughness of t... | Summary: The paper presents a Transformer-based framework designed for predicting the aerodynamic drag coefficient of automotive designs directly from 3D vehicle models. This work is motivated by the high computational costs and inefficiencies of traditional Computational Fluid Dynamics (CFD) simulations and wind tunne... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s insightful and constructive comments. Here, we respond to each concern in detail.
**Note to reviewers:** We conducted additional experiments and analyses in response to your valuable suggestions. These results will be explicitly included in the revised manu... | Summary: In this paper, the authors propose DragSolver—a method to effectively estimate physical properties of shapes, such as cars, without the need to run expensive CFD simulations, allowing for the design of novel shapes much faster and more effectively. The proposed method consists of four major blocks: (1) multi-s... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s positive evaluation and constructive summary, which greatly encourages us.
**Note to reviewers:** We conducted additional experiments and analyses in response to the valuable suggestions from other reviewers. Detailed experimental configurations are provide... | null | null | null | null | null | null | null | null |
Efficient Generative Modeling with Residual Vector Quantization-Based Tokens | Accept (poster) | Summary: This paper proposes an efficient generative framework (ResGen) to model residual tokens that has an additional depth dimension in the code sequences. Specifically, ResGen adopts the masked generative framework to sample tokens, and leverages Gaussian mixtures to directly predict the sum of masked token embeddi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive feedback. We address the specific weaknesses and questions raised below:
**Regarding W1: Isolating the Efficiency Gains of Depth Modeling**
We agree that comparing ResGen (masked generation) directly with RQ-Transformer (autoregressive generati... | Summary: This paper proposes ResGen, an efficient RVQ-based generative modeling for balancing quality and efficiency. It involves a masked token modeling strategy similar to MaskGiT, and a multi-token prediction pipeline inspired by CLaM-TTS, in a discrete diffusion process and variational inference. Experimental resul... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their detailed and constructive feedback. We appreciate the recognition of ResGen's motivation and its potential to address computational costs in RVQ-based generation. We address the specific concerns and questions below:
**Concerning the fair comparison with ... | Summary: This paper introduces ResGen, an efficient generative modeling method that uses Residual Vector Quantization (RVQ) for high-fidelity data generation. Its key innovation lies in predicting collective token embeddings rather than individual tokens, which decouples inference complexity from quantization depth.
C... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thorough assessment, positive feedback on our claims, methodology, and supplementary material, and for recognizing the conceptual innovation of ResGen. We appreciate the constructive suggestions for further improvement.
Regarding the points raised:
**Lim... | Summary: This paper introduces ResGen, an efficient generative model leveraging residual vector quantization (RVQ). While RVQ typically enhances image fidelity by increasing quantization depth, it also demands more inference steps during sampling. Instead of sequentially predicting tokens at each depth, ResGen proposes... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive evaluation and constructive feedback.
We address the specific points raised below:
**Essential References Not Discussed (HART):**
Thank you for bringing HART to our attention. We agree it is a relevant recent work and will incorporate a discussion... | Summary: This paper introduces ResGen, a method that directly predicts vector embeddings for groups of tokens rather than individual tokens. This design reduces the number of inference steps, thereby improving latency. Token masking is employed during training, while multi-token prediction is utilized during inference.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments, which help improve our work's clarity. We address each point below:
\
**Q1: Rationale for Predicting Quantized Discrete Tokens vs. Continuous Embeddings**
Our approach of predicting discrete RVQ tokens via cumulative embeddings offers signific... | null | null | null | null |
Exact Recovery of Sparse Binary Vectors from Generalized Linear Measurements | Accept (poster) | Summary: The paper considers a new problem setting of recovering sparse binary vector from generalized linear measurements. The authors propose a simple algorithm based on Plan et al. (2017) and prove its performance guarantee, complemented by a nearly tight lower bound. This then implies tight resolution to noisy 1-bi... | Rebuttal 1:
Rebuttal: Thank you very much for a careful review and all your interesting insights and questions.
We completely agree with the suggestions, minor issues and typos mentioned. We will incorporate them in the next revision of the paper. In particular, we will fix all the minor issues and typos, incorporate t... | Summary: The paper studies the problem of recovering sparse binary vectors from noisy generalized linear measurements. For simplicity I am stating the special case problems SparseLinearReg and 1bCSbinary here: $x \in$ {$0,1$} is the unknown $k$-sparse vector that needs to be estimated. $A \in \mathbb{R}^{m \times n}$ i... | Rebuttal 1:
Rebuttal: Thank you very much for your careful and positive review of the paper. Regarding your question, for the 1bCSbinary problem, there is no computational-statistical gap, since the information theoretic lower bound matches with the sample complexity of the linear estimator (up to constants). However ... | Summary: This paper addresses the problem of recovering a $k$-sparse binary vector from generalized linear measurements. Given observations $y = (y_1, \dots, y_m)$, which are related to a sparse vector $x$ through an inverse link function $g$ such that:
$$
\mathbb{E}[y_i | A_i] = g(A_i^T x), \quad \text{for each } i \... | Rebuttal 1:
Rebuttal: We thank you for your constructive and positive review.
Thanks also for suggesting the additional reference. We will cite this paper as a relevant lower bound for sparse recovery. Since the input signal is binary in our case, the lower bounding techniques are somewhat different, as we can use in... | null | null | null | null | null | null | null | null |
QLASS: Boosting Language Agent Inference via Q-Guided Stepwise Search | Accept (poster) | Summary: Given the limitation that current agent task do not possess high-quality granular reward signals, this work proposes the QLASS (Q-guided Language Agent Stepwise Search) method to automatically explore states , learn step-wise values, and apply these value-based heuristics are inference time. QLASS is shown eff... | Rebuttal 1:
Rebuttal: Dear Reviewer agwr,
We greatly appreciate your insightful comments for our work. Here are our responses to your questions.
> 1 Incomplete computation cost calculation and explanation on “Completion tokens”
We would like to clarify that all the inference-time methods in Figure 3, i.e., Best-of-N... | Summary: This paper proposes QLASS, a method for Q-value estimation in process reward modeling, providing stepwise guidance for language agents. QLASS consists of four main stages: SFT to train the LLM agent, exploration tree construction, QNet training and Q-guided generation. Compared to multiple baselines, QLASS ach... | Rebuttal 1:
Rebuttal: Dear Reviewer hiva,
We sincerely thank you for the constructive suggestions and positive feedback. We will address your concerns below.
> W1: Lack of experiments of applying QLASS to different LLM agents
We understand that the reviewer encourages us to experiment on diverse LLM agents to valida... | Summary: The paper proposes an LLM self-improvement recipe for tasks where there is a (possibly sparse) external verification signal, inspired by the Q-learning algorithm for Markov Decision Processes. Experiments conducted on three domains (ALFWorld, SciWorld and WebShop) show that the proposed recipe can yield perfor... | null | null | null | null | null | null | null | null | |
TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree | Accept (poster) | Summary: This paper proposes a novel continuous learning approach, TreeLoRA (K-D Tree of Low-Rank Adapters), which exploits hierarchical gradient similarity to build layer-wise adapters for efficient CL.To achieve even greater efficiency, the authors develop a confidence lower bound based bandit techniques to efficient... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's constructive feedback. In the following, we respond to each question.
---
**Q1.** "Regarding the use of LCB to calculate the similarity between tasks especially in transformer-based models, is it computed only at the last layer of the model, or is each laye... | Summary: TreeLoRA presents a continual learning method that enhances the efficiency of updating large pre-trained models. By integrating layer-wise LoRA with a hierarchical gradient similarity tree, it improves knowledge retention while reducing computational costs. TreeLoRA mitigates catastrophic forgetting while main... | Rebuttal 1:
Rebuttal: Thanks for your constructive and helpful comments. We provide our response to each question as below.
---
**Q1.** "The paper presents evidence for TreeLoRA's effectiveness but lacks discussion on the stability and robustness of its tree structure over extended task sequences. A deeper analysis o... | Summary: This paper proposes TreeLoRA, a novel and efficient approach for continual learning in large pre-trained models. TreeLoRA constructs a hierarchical tree structure of LoRAs based on gradient similarity, enabling efficient task adaptation and knowledge sharing. The method employs bandit algorithms to explore tas... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's feedback. In the following, we address each of your technical inquiries.
**Q1.** "impact of task order on the performance ...this paper does not use the same dataset used by O-LoRA."
**A1.** Thank you for your comment. First, we would like to clarify that t... | Summary: This paper proposes TreeLoRA, a continuous learning method that builds hierarchical adapters based on gradient similarity, which aims to solve the computational efficiency problem in continuous learning of large pre-trained models (LPMs). By organizing tasks into a K-D tree structure and introducing sparse gra... | Rebuttal 1:
Rebuttal: Thanks for your helpful comments! Below, we address your major technical questions and will revise the paper to improve clarity and resolve any potential misunderstandings.
---
**Q1.** Elaborate more on the construction of the tree structure, including update and expansion, threshold design, rel... | null | null | null | null | null | null |
What can large language models do for sustainable food? | Accept (poster) | Summary: This paper explores the potential of large language models for sustainable food science. Specifically, this paper evaluates LLMs on four tasks, including experimental design, menu design, sensory profile prediction and recipe preference prediction. Then, this paper equips LLMs with combinatorial optimization t... | Rebuttal 1:
Rebuttal: We greatly appreciate your feedback.
# Fit for ICML
Thank you for raising this point. We believe that this paper is a strong fit for the Application-Driven Machine Learning track of ICML, defined as papers that “introduce novel methods, datasets, tasks, and/or metrics according to the needs of a r... | Summary: This paper explores the capabilities of Large Language Models (LLMs) in a set design of prediction tasks associated with sustainable diets (mainly plant-based) that were based on sustainable food literature and collaboration with domain experts. The overall objective of the tasks consists of generating low-emi... | Rebuttal 1:
Rebuttal: We greatly appreciate your feedback.
# Statistical analysis
Thank you for raising this point. We note that we could have analyzed this data in alternative ways, given both the small sample size ($n=60$ ratings across 30 products) and clustering in the data (namely, pairs of products that were eval... | Summary: The paper investigates how LLMs can help reduce environmental impacts associated with food production. It establishes a typology of tasks relevant to sustainable food development, specifically focusing on design and prediction tasks at various levels (ingredients, recipes, and food systems). Evaluations of var... | Rebuttal 1:
Rebuttal: We greatly appreciate your feedback.
# Baselines
We have added two baselines (expert chef, and the beef to chicken substitution you suggest), both of which we outperform, and three ablations (remove preferences component, remove diversity component, and remove both) for the menu design task. The c... | Summary: This paper explores the potential of Large Language Models (LLMs) in addressing sustainability challenges in food systems. The authors define a typology of tasks related to sustainable food, including design and prediction tasks at the ingredient, recipe, and system levels. They evaluate six LLMs on four speci... | Rebuttal 1:
Rebuttal: We greatly appreciate your feedback.
# Theoretical claims
A proof is in Appendix A. YVD5 noted that the bound can be improved, which we will incorporate into the final version.
# Related work
Thank you, we will cite these works. Please see our response to XTjM, where we discuss these papers; we di... | Summary: This paper investigates how LLMs can contribute to developing sustainable food options (e.g., reducing greenhouse gas emissions). The authors define a typology of design and prediction tasks for sustainable food at three resolutions (ingredients, recipes, and food systems ). The paper focuses on four tasks: 1)... | Rebuttal 1:
Rebuttal: We greatly appreciate your feedback.
# Ablations and analysis
Our submission included two ablations - removing the IQP component entirely (just prompting o1-preview directly to revise the menu) and removing the descriptions. We add three other ablations: removing the estimated preferences, removi... | null | null | null | null |
Constrained Belief Updates Explain Geometric Structures in Transformer Representations | Accept (poster) | Summary: In this paper, the authors propose a theoretical framework suggesting that transformers implement constrained Bayesian belief updating, and explain observed geometric patterns in transformer representations. Using the tools of mechanistic interpretability, they derive and empirically validate precise predictio... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and insightful feedback. We appreciate your recognition of our works rigor and the empirical support in our controlled setting. We also acknowledge the validity of your concerns regarding quantitative evaluation, learning dynamics, scope, and unexplained deviatio... | Summary: This paper studies internal representations in transformers trained with next-token prediction on sequences generated by Hidden Markov Models. It reveals that transformers (focusing on single-layer transformers) perform constrained Bayesian belief updates, implementing an approximate version of optimal Bayesia... | Rebuttal 1:
Rebuttal: Thank you for the positive assessment of our work, and also your constructive feedback, which improved the clarity and impact of our work.
We address your primary concerns regarding the single-layer focus and the justification for Eq. 5, as described below, with new figs and analysis:
**Single La... | Summary: This work bridges mechanistic interpretability and Bayesian definitions of optimal predictions (constrained belief updating) to study emerging geometric structures in small (1-layer) transformer models. This is done with a solid theoretical foundation, in a ‘controlled’ experiment: We know the generative model... | Rebuttal 1:
Rebuttal: Dear Reviewer bbxf,
Thank you for your positive assessment and thoughtful feedback. We appreciate that you find the work well-motivated and structured. We care very much about making the paper understandable and clear, so your suggestions for clarification are very helpful!
**Emergence of Fracta... | Summary: This paper discussed the circuit implemented within trained Transformer to implement (partial) Bayesian inference on a type of Hidden Markov Model. The paper combines theoretical analysis with interpretability tool to showcase that the implemented circuit and corresponding representation is optimal when accoun... | Rebuttal 1:
Rebuttal: Thank you for your strong endorsement of our work. We appreciate your positive assessment of our paper's strengths, and we agree that this paper should make a nice contribution to both interpretability and the study of representations.
We also appreciate your constructive feedback, which nudged us... | null | null | null | null | null | null |
Elucidating Flow Matching ODE Dynamics via Data Geometry and Denoisers | Accept (poster) | Summary: This paper gives a theoretical characterization of the convergence behavior of flow model ODE trajectories. The authors show that flow trajectories can be divided into three stages -- initial stage where particles are attracted towards dataset mean, intermediate stage where particles are attracted towards loca... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and constructive comments. Thank you for appreciating the convergence guarantees and insights into the memorization behavior of flow and diffusion-based generative models. We will fix the missing parenthesis in the definition of the medial axis to ma... | Summary: This paper studies the convergence behavior of the ODE trajectories in flow-matching w.r.t training data using analytical tools from geometry. Specifically, it provides an extensive analysis of the ground-truth FM ODE trajectories under the affined Gaussian path, and shows how trajectories are shaped by data g... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive feedback and recognition of our novel theoretical contributions on the FM ODE convergence under weak assumptions, per-sample trajectory evolution patterns, and connections to memorization. We believe that the per-sample level analysis is important as it could ... | Summary: This paper presents a theoretical analysis of Flow Matching (FM) models addressing how data geometry influences the dynamics of the ODE trajectories used in FM-based generative models. They show that the denoiser guides the ODE dynamics through attracting and absorbing behaviors. They identify three stages of ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and for recognizing the theoretical importance of our work on the convergence of FM ODE dynamics, the analyses connecting data geometry with FM model trajectories, and the memorization phenomenon. Below, we address the main comments/questions:
*... | null | null | null | null | null | null | null | null |
Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics | Accept (poster) | Summary: This paper analyzed categorical TD learning with KL loss in the tabular setting. They also proposed a preconditioned version of the algorithm and provided an asymptotic normal analysis. They also conducted experiments to verify the theoretical results.
Claims And Evidence: Yes
Methods And Evaluation Criteria... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time and effort in reviewing our paper, and we are pleased that they found our motivation compelling, appreciated our use of preconditioning to obtain a convergence guarantee, and found our use of asymptotic variance for the analysis to be a clear choice.
**C... | Summary: In this paper, the authors studied the theoretical properties of categorical distributional TD with KL loss. They proposed a preconditioned version of the algorithm called PKL-CTD, proved its asymptotic convergence, and derived the asymptotic distribution of the resulting value estimators. These theoretical re... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort in reviewing our paper, and for the helpful feedback and suggestions provided. We are pleased to hear that they found our work to provide valuable insights for practitioners and our idea of preconditioning to be concise and intuitive.
**Asymptotic a... | Summary: This paper revisited categorical distributional RL and conducted the analysis based on KL divergence instead of the conventional Cramer distance. The authors proposed a variant of the algorithm by preconditioning and showed its convergence. More importantly, the asymptotic normality or asymptotic variance is a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort they spent in reviewing our paper, and we aim to resolve their questions below.
**Why do the authors consider the KL variant of categorical distributional RL, and what is the gap between it from the original algorithm, such as C51?**
We focus on th... | Summary: The paper studies categorical distributional reinforcement learning with a KL divergence loss. Unlike previous analyses relying on the Cramér distance, this paper introduces a novel preconditioned version of categorical temporal-difference learning with KL divergence , proving its convergence under mild assu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort in reviewing our paper, and for the helpful feedback provided. We are pleased that the reviewer found our work on the convergence of PKL-CTD, the asymptotic variance analysis, and the connection between theory and empirical observations to be valuabl... | null | null | null | null | null | null |
Provable Policy Gradient for Robust Average-Reward MDPs Beyond Rectangularity | Accept (poster) | Summary: The studies very sparsely studied topic of average reward robust MDPs. Specifically, the paper establishes global convergence of robust policy gradient (RPG) for average reward MDPs with an iteration complexity of $O(\epsilon^{-4})$ given oracle access to the robust gradient. This paper combines the techniques... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions and taking the time to assess our manuscript.
1. **_Comment 1: Additional clarification in proofs could be helpful ._**
Thank you very much for providing helpful suggestions on writing style and missing clarification! We will clarify the wording in the nex... | Summary: This paper studies robust *average-reward* MDPs (RAMDPs) with general ambiguity sets. It proposes a policy-gradient-based algorithm, RP2G, that leverages an exponentially decaying adaptive tolerance mechanism $\{ \delta_t \}$ to enable provably efficient policy updates, assuming an oracle that solves the inner... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review our paper and your insightful comments as well as suggestions.
**_Comment 1: Technical details in Section 4.3 should be added._**
Thank you so much for pointing out the missing details in this section! That would be definitely helpful if full tec... | Summary: The paper investigates methods for solving robust Markov Decision Processes (MDPs) under the average-reward criterion. Building on existing approaches developed for the discounted-reward setting, the authors extend these ideas to the average-reward framework. The study presents multiple algorithms tailored to ... | Rebuttal 1:
Rebuttal: We are sincerely grateful to the reviewer for the insightful comments and valuable questions.
**_Comment 1. Additional clarification on the difference between our work and Li et al. (2023) is needed._**
We apologize for the insufficient explanation of the contributions of this work. It is impor... | Summary: This paper extends the work of Li et al. 2023 from solving robust MDPs in discounted setting to average reward setting. Numerical results are also provided.
## update after rebuttal
I thank the authors for their efforts in writing the rebuttal. I agree with that letting the discount factor to 1 would of cours... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions and constructive suggestions!
**_Comment 1: Additional comparison between rectangular and non-rectangular RAMDPs would be helpful._**
Thanks for the suggestion! As the (non-)rectangularity only affects the ambiguity structure and appears to be independent... | null | null | null | null | null | null |
The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text | Accept (poster) | Summary: This paper argues that the synthetic data generated by LLMs, which are finetuned on private data, poses privacy risks. They identify a new class of canaries suited for these data-based privacy risks, and show that by choosing an in-distribution prefix and out-of-distribution suffix they can greatly increase th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We provide responses for the concerns raised below.
> (1) one could imagine finetuning on D_tilde and then applying standard MIAs to the finetuned model
Many thanks for pointing this out. We have opted to train an n-gram model on the synthetic data rath... | Summary: The paper investigates the privacy risks associated with releasing synthetic data generated by Large Language Models (LLMs). It explores how much information about the original training data can be extracted from such synthetic data, even when adversaries do not have direct access to the fine-tuned model.
- ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We provide responses for the concerns raised below.
> (1) A minor limitation is that the experiments are conducted on only two datasets
We provide results for the *n*-gram based MIA for the SNLI dataset (for the setup from Table 1) below and will includ... | Summary: This paper proposes to audit the privacy risks from synthetic data generated by LLMs as synthetic data is becoming increasingly prevailing in different applications. The authors found that the typical canaries designed for model-based auditing were not effective for auditing the synthetic data. The paper propo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We provide responses for the concerns raised below.
> (1) analyzing how different domains might impact the auditing efficiency
In Table 1, we also study the effect of which labels (or domains) the canaries belong to. In particular, we consider both ‘nat... | Summary: This paper aims at investigating the privacy risks of synthetic text generated by LLMs by developing a new membership inference attack (MIAs). The main novelty of the proposed approach is that the adversary model considered only has access to the synthetic text generated by the model and not the model itself. ... | Rebuttal 1:
Rebuttal: Thanks for the feedback; we provide detailed responses below.
> Difference between model-based and data-based MIAs supported only through experiments on two datasets. A large set of variations of these experiments have been conducted, which demonstrate the robustness of the proposed approach.
W... | null | null | null | null | null | null |
Proactive Agents for Multi-Turn Text-to-Image Generation Under Uncertainty | Accept (poster) | Summary: This paper focuses on the information-missing issue of existing AI applications, specifically image generation in the paper. Instead of passively waiting for humans to revise the prompt, this paper proposes a proactive design such that the agent system could proactively interact with the users to get the missi... | Rebuttal 1:
Rebuttal: > The current brief graph requires heavy human design, which will limit the generalization of the proactive agent system to other domains other than the image generation task. How do you prevent that?
Generating the belief graph only requires some few-shot examples, which are not difficult to wri... | Summary: This paper addresses the challenge of underspecified user prompts in text-to-image (T2I) generation by introducing proactive agents capable of multi-turn interactions. These agents actively seek clarification through targeted questions and utilize a "belief graph" to represent and refine their understanding of... | Rebuttal 1:
Rebuttal: > The universal prompt design: It seems like the design of the prompt lacks consideration of the alignment and prompt-following capabilities of the text-to-image model. From my personal user experience with T2I models, the correct wording and structure of the prompt may also greatly influence the ... | Summary: This paper proposed a proactive text-to-image agent designed to mitigate the issue of uncertain prompts by allowing users to do multi-turn interactions. The key contributions of the paper include:
1. Belief Graphs – A structured representation of model uncertainty, allowing users to visualize and edit entitie... | Rebuttal 1:
Rebuttal: > baseline
We used only one single-turn T2I baseline because we want to keep the T2I backbone consistent between the baseline and all agents. To ensure the consistency, we will run all agent experiments with a different model (such as the suggested ones) and we can definitely add this comparison ... | Summary: This paper addresses the issue of suboptimal image generation caused by vague or incomplete prompts provided by users to text-to-image (T2I) generators. It introduces proactive T2I agents designed to improve image generation by actively asking users clarification questions. These agents maintain their understa... | Rebuttal 1:
Rebuttal: > Q1: “Is the capability of the whole pipeline limited by the prompt-following capabilities of the text-to-image model?” Also in Experimental Designs Or Analyses: “misalignment between the textual exploration and the image generation.”
The current agent prototypes call off-the-self T2I models dir... | null | null | null | null | null | null |
ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone Generation | Accept (poster) | Summary: The paper introduces ReQFlow, a novel method for protein backbone generation. To address numerical instability in matrix-based representations, the main innovation is to use quaternions to model the rotations and spherical linear interpolation in the flow matching training. The paper also extends the rectifi... | Rebuttal 1:
Rebuttal: Thanks for your comments.
**Correction of long-chain results:** Before resolving your concerns, we would like to report that we found a bug in our script in the rebuttal phase and correct the results of ReQFlow on long-chain generation in the anonymous link https://anonymous.4open.science/r/6342_... | Summary: This paper focuses on the task of protein backbone generation. It proposes quaternion flow (QFlow) and rectified quaternion flow (ReQFlow) for generative modeling on a translation/rotation manifold. In particular, in contrast to previous work, QFlow models the rotations with quaternions and the authors introdu... | Rebuttal 1:
Rebuttal: Thanks for your positive feedback and constructive comments.
**Correction of long-chain results:** Before resolving your concerns, we would like to report that we found a bug in our script in the rebuttal phase and correct the results of ReQFlow on long-chain generation are in https://anonymous.4... | Summary: The paper proposes a method to train a generative model of protein backbones. They follow previous work in parameterizing protein backbones using a translation and a rotation. They have two main contributions:
- The use of normalized quaternions to parameterize rotations (most previous work use 3x3 rotation m... | Rebuttal 1:
Rebuttal: Thanks for your positive feedback and constructive comments.
Before resolving technical concerns,
**1) Apology for reusing sentences in Proteina.** We sincerely apologize for reusing sentences from Proteina in Appendices B.5 and B.6 without proper citation. We read this paper when it was under ... | Summary: This paper introduces a new flow matching method for unconditional protein backbone generation, based on quaternion representations and rectified flows.
More specifically in this work, the rotational part of the backbone residues is represented as unit quaternion, instead of an SO(3) matrix which is the canoni... | Rebuttal 1:
Rebuttal: Thanks for your positive and constructive comments.
**Correction of long-chain results:** Before resolving your concerns, we would like to report that we found a bug in our script in the rebuttal phase and correct the results of ReQFlow on long-chain generation in the anonymous link https://anon... | null | null | null | null | null | null |
On Volume Minimization in Conformal Regression | Accept (poster) | Summary: The authors present a method that minimizes the volume of a conformal prediction region, subject to the coverage being the desired one (1- alpha). They show its theoretical properties, and its empirical validity.
Claims And Evidence: The claims are clear and convincing. I'd like the authors, though, to addres... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their valuable feedback and for pointing out relevant related work. Below, we address each of their questions and comments.
- *Q1 In https://link.springer.com/book/10.1007/b106715, Theorem 2.10, the authors provide a proof by construction, that can be used to ... | Summary: The paper theoretically studies the efficiency of CP sets, providing bounds for the specific task of regression. The bounds are given assuming a fixed base predictor, or in the case where the predictor is learned on held-out data (split CP). For the latter case, the authors also highlight the importance of min... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful feedback and the references provided. We acknowledge that some relevant prior works were overlooked, and we will incorporate them into the related work section to ensure a more comprehensive discussion. While we recognize that this may slightly ref... | Summary: **Post-rebuttal edit:** The authors wrote convincing answers to my two grounds and questions. I keep my positive score.
The submission considers (split) conformal prediction for univariate data, with scores given by absolute values of the residuals, and with prediction regions given by intervals of t... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and constructive feedback. We are especially grateful for their recognition of the clarity and novelty of our contribution, particularly our use of statistical learning theory in the context of split conformal prediction. We will incorporate the... | Summary: Conformal prediction is a framework to construct label sets such that the marginal probability of coverage is guaranteed to be above a desired level $(1 - \alpha) \in (0, 1)$. This paper studies the conformal label intervals (one contiguous set) for unidimensional regression problems. The motivation is to mini... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their constructive feedback and insightful questions. Below, we address each point in detail. We also appreciate the suggestions regarding our real-world data experiments and the approximation of the indicator function—these will be included in the final version... | null | null | null | null | null | null |
Global curvature for second-order optimization of neural networks | Accept (poster) | Summary: The submission studies the structure of the "global curvature" of deep networks. The main result is that global matrix quantities such gradient covariance and Hessian (where "global" means the expected values of those matrices under some distribution on the weights) has a specific matrix structure with much fe... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detail comments that will improve our paper.
- *Why global curvature*. We agree on the lack of clarity for why global curvature should be used as a preconditioner. We will revise as follows:
Introduction: “The primary motivation for considering global curvature is it... | Summary: The work attempts to improve the computations of second order methods by analyzing the covariance matrix of the gradients in small MLP networks. They rely on certain symmetrics expected to be in network parameters and derive theory on the structre, as well as explicit solutions for the covariance matrix. They ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments, which will help improving our work.
We would like to highlight that the main contribution of our work is theoretical. As acknowledged by the other reviewers, the theory presented in our work is novel and it could be of broad interests for other fields of ma... | Summary: The authors' work focuses on getting insights on the second moments of $\Sigma_t = \int \nabla \mathcal L(\theta)\nabla \mathcal L(\theta)^\top dp_t(\theta) - \mu_t \mu_t^\top$, by exploiting invariances in representation space. In particular, the authors observe that if $G$ is a symmetry of the loss function... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments. We are glad that the reviewer recognises that the key contribution of our work is theoretical and considers our work novel and interesting.
We answer here the main concerns raised by the reviewer.
- *Empirical comparison with other second-order meth... | null | null | null | null | null | null | null | null |
CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance | Accept (poster) | Summary: The paper introduces CodeSteer, a method to guide large language models (LLMs) in making optimal choices between textual reasoning and code generation. The authors propose a multi-round supervised fine-tuning (SFT) and direct preference optimization (DPO) approach using a newly created dataset. A benchmark, Sy... | Rebuttal 1:
Rebuttal: Thank you for your appreciation with our work and helpful suggestions. Here are our responses and newly added experiments based on reviewers’ questions. We kindly ask the reviewer to reconsider our work in light of the responses below. We are happy to further communicate with the reviewer.
***Que... | Summary: This paper targets the better performance of LLMs on symbolic reasoning tasks such as Game-24 with multi-round generations. The method contains many components, such as a small model fine-tuned for guiding the generation, a self-answer checker, and a hardcoded symbolic checker.
The guidance model (CodeSteer ... | Rebuttal 1:
Rebuttal: Thank you for your appreciation with our work. The following responses clarify several misunderstandings raised by the reviewer. We've also incorporated new experiments and analyses. We kindly ask the reviewer to reconsider our work in light of the responses below.
***Question 1:*** *Test on 9 u... | Summary: This work introduces a comprehensive benchmark SymBench comprising 37 symbolic tasks with adjustable complexity
and also datasets of 12k multi-round guidance/generation trajectories and 5.5k guidance comparison pairs, and fine-tune a CodeSteerLLM using the introduced datasets, achieving improved reasoning perf... | Rebuttal 1:
Rebuttal: Thank you for your appreciation with our work and helpful suggestions. We've incorporated new experiments and analyses based on reviewers’ questions. We kindly ask the reviewer to reconsider our work in light of the responses below.
***Question 1:*** *The authors train the CodeSteerLLM to verify ... | Summary: This paper introduces **CodeSteer**, a model fine-tuned to enhance reasoning abilities between text and code. The authors propose a synthetic dataset and a new evaluation suite of 37 symbolic tasks to demonstrate the model’s performance on complex reasoning tasks. They claim that by leveraging both code-genera... | Rebuttal 1:
Rebuttal: Thank you for the helpful feedback. We've clarified several misunderstandings raised by the reviewer and also incorporated new experiments and analyses. Hope the reviewer could reconsider our work based on the responses below.
***Question 1:*** *Direct comparison with models that use text or code... | null | null | null | null | null | null |
CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations | Accept (poster) | Summary: This paper proposes the CMoS, a highly lightweight model for time series forecasting tasks. Unlike previous studies, CMoS capture the temporal patterns in a chunk-level manner. The Correlation Mixing mechanism builds robust correlationmatrices, and Periodicity Injection technique help to leverage the periodi... | Rebuttal 1:
Rebuttal: **Thank you for your detailed and thoughtful review! We will address your concerns point by point.**
> Q1: The statement may not hold when time series data shows no periodicity.
We appreciate your careful examination of this matter. **Indeed, as you pointed out, the formulation becomes less rigo... | Summary: This paper works on time series forecasting task, and the main idea is to split time series into chunks, and build up chunk-chunk spatial correlations to achieve robust time series forecasting. The paper is clearly written, the proposed modules are accompanied with good motivations and solid proofs.
## Update... | Rebuttal 1:
Rebuttal: **Thank you for your detailed and thoughtful review! We will address your concerns point by point.**
> Q1: Details about Fig. 1
As an illustrative example, we use the MSE of each pair of chunks as the correlation value (labeled in the figure), and a lower MSE meaning stronger correlation. **Only... | Summary: This paper presents CMoS, a super-lightweight time series forecasting model that utilizes chunk-wise spatial correlations to achieve parameter-efficient and interpretable predictions. The key innovation lies in directly modeling the spatial dependencies between time blocks of fixed size, rather than point-orie... | Rebuttal 1:
Rebuttal: **Thank you for your detailed and thoughtful review! We will address your concerns point by point.**
> Q1: How could Theorem 3.2 be extended to nonlinear dependent or non-Gaussian noise (e.g., burst noise), and what empirical evidence supports its robustness under such conditions?
- **Extended t... | Summary: There's a recent line on making small architectures that match the performance of large deep learning models for TS forecasting, which raises the question of the relevancy of DL for time series forecasting. The authors propose CMoS, a novel architecture for time series forecasting that is very lightweight. The... | Rebuttal 1:
Rebuttal: **Thank you for your detailed and thoughtful review! We will address your concerns point by point.**
> Q1: Novelty of some components
Your example is quite helpful! So we explain the two aspects you mentioned following the format you provided:
- The use of ConvNet.
Existing methods like DeepTC... | null | null | null | null | null | null |
Where is the Truth? The Risk of Getting Confounded in a Continual World | Accept (spotlight poster) | Summary: The paper explores a nuanced aspect of continual learning related to confounding data. It highlights how confounding data can create shortcuts by fostering spurious correlations, ultimately hindering the generalization ability of continual learning methods. The authors demonstrate the effects of confounding da... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. In the following, we would like to address their two concerns.
**Synthetic vs Real-World Datasets**
In our paper, we introduce a real-world experiment on the popular ImageNet dataset and observe a surprising discrepancy between joint and cumulative train... | Summary: This paper introduces the concept of continual confounders. A data contains confounders when a model trained on the data can fit the training data using spurious correlations but fails to generalize at test time. Continual confounders are ones that control distributions across a continual set of tasks. The pap... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and positive evaluation of our work. We would like to comment on a few points in their review.
**Related Work on Distribution Shifts**
Thank you for suggesting further related work. We have included the references and discussed their relation to ConCon an... | Summary: The paper presents the confounder problem in the continual learning regime, which is novel. It also establishes a benchmark with clear logical definitions and potentially highlights a new direction for studies in the continual learning field to improve the overall performance.
Claims And Evidence: The claims ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and we will include the suggested references in our related work section. We are happy to read that they agree that our experiments are sufficient to support our claims. Nevertheless, we invite the reviewer to also take a look at our responses to t... | Summary: This paper explores confounding in continual learning. The authors formally describe confounding factors that lead to poor generalization and introduce a CLEVR-based synthetic dataset (ConCon) to study these challenges. They evaluate several continual learning approaches on ConCon and show that these methods s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and constructive feedback. We first try to clarify their more general concerns.
Features that act as confounders in one dataset can also appear as random features in other datasets. The distribution shift between the confounded tasks and the unconfounded dat... | null | null | null | null | null | null |
General framework for online-to-nonconvex conversion: Schedule-free SGD is also effective for nonconvex optimization | Accept (oral) | Summary: This work investigates the effectiveness of schedule-free methods in nonconvex optimization. The authors first develop a general framework for online-to-nonconvex conversion, which converts a given online learning algorithm into a nonconvex optimization algorithm. This framework not only recovers existing conv... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful question. We agree that a deeper understanding of optimizer behavior ultimately requires incorporating finer-grained properties of the training loss landscape.
That said, a somewhat surprising takeaway from our work—especially in light of your comment—is that certain ... | Summary: This paper develops a general framework for online-to-nonconvex conversion, which reduces the problem of finding a stationary point of a non-convex objective function to an online learning problem. Their framework extends the work of Zhang & Cutkosky (2024) with a tighter analysis, and it also leads to two new... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments!
**Regarding the step size comparison.**
Thank you for pointing this out. Indeed, the step size of our Schedule-Free algorithm should not be compared with the step size of the OMD, and instead with the ``effective'' step size of the momentum method of Zh... | Summary: This paper introduces a more general online-to-nonconvex reduction. Based on the an OMD variant with discounted regret guarantees, the optimal convergence rate to a $(\lambda,\delta)$-stationary point is shown for three different variants. The third variant is shown to coincide with schedule-free SGD algorithm... | Rebuttal 1:
Rebuttal: Thank you for encouraging feedback!
We do agree that designing and analyzing algorithms for the nonconvex and non-smooth setting is a very interesting direction.
We also agree that it's nice to see have strong theoretical guarantees for practical optimizers. | Summary: This paper presents a general framework for conversion of any online-learning algorithm into a non-convex (non-smooth) optimization algorithm. The authors provide general non-convex convergence guarantees for the online-to-nonconvex unified framework in terms of the $(\lambda, \epsilon)$-stationary point. Thei... | Rebuttal 1:
Rebuttal: Thank you for your feedback and suggestions.
- The choice of these comparator sequences can be motivated by viewing them as the "good" update direction in hindsight. If we ignore EMA averaging in the choice of comparators (for simplicity), we can see that the comparator points exactly in the d... | null | null | null | null | null | null |
Strategy Coopetition Explains the Emergence and Transience of In-Context Learning | Accept (oral) | Summary: This paper systematicallly studies transient dynamics of in-context learning (ICL) in transformers. In particular, the authors identify that after ICL disappears, a hybrid strategy between in-weights and in-context learning called "context-constrained in-weights learning" (CIWL) emerges, which competes with an... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and are glad they found our work "particularly compelling". We've factored in their suggestions and respond to their question below:
The mathematical model presented was largely just our first attempt at extending the "single mechanism" model from Singh et a... | Summary: Architecture: 2-layer attention-only transformer (appendix has other models)
Dataset: Omniglot, augmented to 12k+ classes. The majority of the classes are used for training, but the remaining 184 classes are used for testing.
Training sequences are set up with a few shot learning favor and constructed using ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and are happy they think our work is "an excellent example of rigorous deep learning science." We've also noted and updated the paper based on the suggestions, with responses to specific questions below:
> How do the authors expect the findings on ICL pers... | Summary: This paper investigates why in-context learning (ICL), a capability that emerges in transformer models without explicit training, sometimes disappears after extended training periods. The authors study this phenomenon using a simplified experimental setup with 2-layer attention-only transformers trained on a s... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review. We really appreciate your acknowledgement of the strong and thorough evidential support for our main claims.
## We respond here to the main criticisms:
> The claim that ICL can be made persistent by matching context and query exemplars is supported... | null | null | null | null | null | null | null | null |
Offline Model-based Optimization for Real-World Molecular Discovery | Accept (poster) | Summary: The authors propose MolStitch as a generative method to generate molecular designs in an offline, multi-objective setting. Their method generates novel 'stitched molecules' that combine the desirable properties of original molecules sampled from the offline dataset. The authors evaluate their method on a numbe... | Rebuttal 1:
Rebuttal: We appreciate your helpful feedback and the opportunity to enhance our manuscript.
# Q1: Additional references would strengthen the experimental results.
First, regarding **Simulated Annealing (SA)**, it is a probabilistic optimization algorithm that enhances exploration by occasionally accepting ... | Summary: This paper introduces the Molecular Stitching (MolStitch) framework, designed to address the molecular discovery problem in an offline setting, where an offline dataset is employed without requiring iterative queries to the oracle function. Particularly, MolStitch operates by leveraging existing molecules from... | Rebuttal 1:
Rebuttal: We are truly grateful for your thoughtful feedback. In the following, we carefully respond to each of your comments.
# Q1: The authors should include recent works
We sincerely thank the reviewer for highlighting these important references [1–5], which are indeed highly relevant from the perspectiv... | Summary: The paper introduces MolStitch, a framework for offline multi-objective molecular optimization (MOMO). Key contributions include StitchNet, which generates "stitched molecules" by combining fragments from an offline dataset; a rank-based proxy model for pairwise molecule evaluation; and preference optimization... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful feedback and the opportunity to clarify the key aspects of our study.
# Q1: Why was SMINA not used for docking evaluation?
In our original study, we employed QuickVina (QVina) [1] for docking score evaluation, which is a widely recognized molecular docking t... | Summary: This paper introduces MolStitch, a framework for offline molecular optimization that generates novel molecules by "stitching" fragments from an existing offline dataset, eliminating the need for iterative oracle queries. Inspired by trajectory stitching in offline reinforcement learning, MolStitch uses StitchN... | Rebuttal 1:
Rebuttal: We are grateful for your thoughtful feedback. Below, we respectfully provide point-by-point responses to each of your comments.
# Q1: Have you explored the Chebyshev scalarization technique?
In our original study, we employed linear scalarization because it is one of the most fundamental scalariza... | null | null | null | null | null | null |
A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks | Accept (poster) | Summary: The authors propose a methodology, named Cognac, aimed at enhancing the fairness, robustness, and accuracy of Graph Neural Networks (GNNs) through corrective unlearning techniques applied to specific nodes. Cognac consists of two primary components:
1. Contrastive Unlearning on Graph Neighborhoods (CoGN): Thi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough and positive assessment of our work! We are thrilled that you recognized the novelty, mathematical soundness, experimental rigor, and effectiveness of our proposed method. In response to the other reviewers, we have added more experiments: (1) a feature tri... | Summary: The paper addresses the challenge of Corrective Unlearning in Graph Neural Networks (GNNs). While GNNs are widely used across various applications, their message-passing mechanism makes them vulnerable to adversarial manipulations and erroneous data, as errors can propagate throughout the graph. To mitigate th... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s recognition of our work, particularly noting our theoretical analysis of adversarial attacks on GNNs, the effective mitigation of data manipulation through Cognac, and its computational efficiency compared to retraining.
---
## Expanding the analysis to a wider range... | Summary: In this paper, the author proposes an unlearning algorithm, Cognac, to remove manipulated data from a well-trained GNN model. The approach first identifies sensitive neighbors that may be influenced by spurious entities and then mitigates these effects by aligning the embeddings of the selected neighbors with ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s recognition of our comprehensive experiments and the soundness of our claims. We're also pleased that the reviewer found the idea behind Cognac innovative. We hope our clarifications below address your concerns.
---
## Affected Nodes Sampling
_Can solely targeting h... | null | null | null | null | null | null | null | null |
Deterministic Sparse Fourier Transform for Continuous Signals with Frequency Gap | Accept (poster) | Summary: The paper introduces the first deterministic algorithm for computing the sparse Fourier transform (SFT) of continuous signals that have a minimum frequency gap. In contrast to earlier approaches that relied on randomness, the authors develop a method that deterministically recovers a k‑sparse signal (i.e., one... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comment and we greatly appreciate the reviewer's recognition of our contributions. We believe that our work represents a substantial advancement in the theoretical understanding of Fourier transforms. We also appreciate the reviewer’s perspective regarding th... | Summary: This paper introduces a deterministic sublinear-time algorithm for recovering sparse continuous signals with frequency gaps, which addresses a critical gap in prior research by random approaches. The proposed method achieves optimal recovery guarantees in the presence of arbitrary noise.
Claims And Evidence: ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the recognition of our theoretical and algorithmic contributions.
### W1, Q1 and Q3: Lack of empirical validation
Thank you for pointing this out. We acknowledge that empirical validation would be beneficial, but our focus in this submission was on establishing theoretica... | Summary: This paper adapts Li and Nakos (2020)'s deterministic sparse Fourier transform (SFT) algorithm to the continuous-time setting described by Price and Song (2015) (who had proposed a randomized algorithm), showing that an efficient deterministic method exists in this regime as well. The proposed algorithm has $O... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and for recognizing the novelty, significance, and technical contributions of our work.
### W1: Self-contained Explanation
We will include clearer explanations of fundamental Sparse Fourier Transform (SFT) methods (such as hashing, filtering, and co... | null | null | null | null | null | null | null | null |
Shifting Time: Time-series Forecasting with Khatri-Rao Neural Operators | Accept (poster) | Summary: The authors propose a time series forecasting method, leveraging continuous time-shift operators, which act as continuous analogs of the lag factor of the discrete-time autoregressive models or the upsampling/downsampling layers in CNNs, mapping the history of values up to an observation to a future window. To... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and valuable comments.
## 1. Claims and Evidence:
>**(Almost) Linear Complexity..**
Please refer to our first response to reviewer **dfXC** for more clarification on the computational complexity and runtime analysis of KRNO compared to FNO.
>**Continuous Analo... | Summary: The paper introduces a novel operator-theoretic approach for time-series forecasting by learning a continuous time-shift operator. This method provides a more flexible alternative to traditional autoregressive models, which rely on discrete time lags. The authors propose Khatri-Rao Neural Operators (KRNOs) as ... | Rebuttal 1:
Rebuttal: Thank you for your feedback and comments.
## 1. Computational Complexity and Runtime Analysis
In Appendix G, we provide a detailed comparison of computational complexity and runtime between KRNO and FNO-3D using the spatio-temporal shallow water problem. While our initial analysis used the defa... | Summary: The paper introduces a novel operator-theoretic framework for time-series forecasting, leveraging the Khatri-Rao Neural Operator (KRNO) to learn continuous time-shift operators. By relaxing the discrete lag factor in autoregressive models, KRNO enables super-resolution forecasting in both space and time while ... | Rebuttal 1:
Rebuttal: Thank you for your feedback and comments.
## 1. Theoretical Guarantees and Kernel Structure
The computational complexity of the kernel integral transform layer scales as O($n^2$), where $n$ is the number of quadrature nodes in the input and output domains. Additional assumptions are required to ... | Summary: This paper presents a method for time-series forecasting that treats the task as learning a continuous time-shift operator, approximated via a proposed architecture called Khatri-Rao Neural Operator (KRNO). The operator is modeled as an integral transform with a non-stationary kernel, decomposed via Khatri-Rao... | Rebuttal 1:
Rebuttal: Thank you for your feedback and comments.
### 1. Evaluation on Irregularly Sampled Datasets
We agree that numerical studies on regularly sampled time-series do not allow a clear demonstration of the full capabilities offered by KRNO. We now include additional experiments on challenging irregularl... | null | null | null | null | null | null |
Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models | Accept (poster) | Summary: The paper addresses object hallucination, a well-known issue for existing multimodal large language models (MLLMs), where they prone to generate plausible yet incorrect responses that are not aligned with given images. Authors attribute this to weak robustness and high uncertainty of LLM representations (secti... | Rebuttal 1:
Rebuttal: **We sincerely thank Reviewer 8UAJ for the insightful comments.**
> Q1: Given that most layers are not aligned with the vocabulary head, what such experiments indicate is doubtful and the conclusions do not make sense...
The idea of applying language heads directly to the hidden states of the mi... | Summary: This paper presents Memory-space Visual Retracing (MemVR), a decoding strategy aimed at mitigating hallucinations in Multimodal Large Language Models (MLLMs). The primary insight is that MLLMs tend to lose visual information during the decoding process, leading to hallucinations due to an over-reliance on text... | Rebuttal 1:
Rebuttal: **We sincerely thank Reviewer 3FtN for the constructive comments on our work. We promise to revise the paper.**
> Q1: About textual uncertainty
Yes, textual uncertainty cannot be a sufficiently necessary condition for the existence of hallucinations, but when hallucinations are occurring, textua... | Summary: This paper addresses the hallucination issue in Multimodal Large Language Models (MLLMs) by proposing MemVR, a novel decoding paradigm. MemVR uses visual tokens as supplementary evidence and re-injects them via FFN at the middle trigger layer. Theoretical analysis shows MemVR can mitigate hallucinations by enh... | Rebuttal 1:
Rebuttal: **We sincerely thank Reviewer bEEH for the constructive comments on our work.
We are very grateful to the reviewer for recognising the novelty of our idea and the richness and rationality of our experiments.**
> Q1: Missing comparisons with cross-attention based retrieval.
Sorry for the confusi... | Summary: This paper introduces Memory-Space Visual Retracing (MemVR), a novel decoding approach to mitigate hallucinations in Multimodal Large Language Models (MLLMs). The authors posit that hallucinations often occur due to the model's tendency to "forget" visual information during text generation, and they address th... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and kind suggestions.
> Q1: The contribution of revisiting visual tokens.
MemVR is fundamentally different from the current approaches, you may refer to Table 2 in the paper, where we show comprehensive comparisons with existing methods.
1) Retrieval-bas... | null | null | null | null | null | null |
Online Curvature-Aware Replay: Leveraging $\mathbf{2^{nd}}$ Order Information for Online Continual Learning | Accept (poster) | Summary: The paper combines experience replay with a second-order optimizer derived from a KL constraint to tackle online continual learning. The paper improves SOTA results on three vision-based online continual learning benchmarks.
Claims And Evidence: The main claims on OCAR improving online continual learning opti... | Rebuttal 1:
Rebuttal: Dear Reviewer, thank you for your insightful review. We are happy to address your comments:
1) **Line 155**: We consider our stream to be arbitrary (we can have a different task for each step). If the objective has changed (task boundary), and we are at a different point in the space, the previou... | Summary: This paper proposes Online Curvature-Aware Replay (OCAR) that leverages second-order information of the loss
using a K-FAC approximation of the Fisher Information Matrix (FIM) to precondition the gradient in OCL.
Claims And Evidence: Overall the claimed information are well supported. By using second-order me... | Rebuttal 1:
Rebuttal: Dear Reviewer, we thank you for your review and your time. We try to address all your concerns:
1) **Other gradient-altering strategies**: In our experiments, we tested A-GEM and LPR, both examples of gradient projection methods. LPR (current SOTA) is a variant of preconditioned SGD, precondition... | Summary: This paper formalizes replay-based Online Continual Learning (OCL) as a second-order online joint optimization with explicit KL-divergence constraints on replay data. It proposes Online Curvature-Aware Replay (OCAR) to solve the problem: a method that leverages second-order information of the loss using a K-FA... | Rebuttal 1:
Rebuttal: Dear Reviewer, thank you very much for your review. We hope to be able to solve your doubts:
1) **OL vs OCL**: We agree that a deeper comparison between Online Learning and Online Continual Learning can be beneficial. In Section 3, we will add a paragraph explaining the main points: both OL and O... | Summary: The paper addressed the online continual learning setting. The authors proposed Online Curvature-Aware Replay (OCAR), a replay-based method that leverages an approximated Fisher Information Matrix to help both the stability and plasticity. The authors further proposed specific adaptation for online continual l... | Rebuttal 1:
Rebuttal: Dear Reviewer, thank you very much for your review and your time. We are happy to answer all your questions:
1) **Additional references**: Thank you for the suggested papers. They are relevant, and we will add a discussion about them in section 2 in the final version.
2) **Classifier Instabilit... | null | null | null | null | null | null |
FG-CLIP: Fine-Grained Visual and Textual Alignment | Accept (poster) | Summary: Based on the observation that CLIP struggles to handle with fing-grained understanding tasks, this paper propose
1. a larger dataset including abundant images, bounding boxes and captions;
2. to incorporate long captions, short captions and hard negative strategies to enhance CLIP's ability during training.
... | Rebuttal 1:
Rebuttal: __1. Response to Weakness 1__
Thanks for pointing out this problem. We perform ablation studies on the number of hard negative samples. Specifically, we test configurations with 1, 5, and 10 hard negative samples per positive sample. Our experiments show that 10 hard negative samples per image-te... | Summary: This paper proposed FG-CLIP, A region level contrastive learning model for fine-grained image representation. Through training on large scale synthetic data. This model achieve strong performance compared to previous methods on fine-grained region-level tasks like fine-grained understanding, OVD, image-text re... | Rebuttal 1:
Rebuttal: __1. Response to Question 1 in Essential References Not Discussed__
Thanks for your comments. We discuss the difference between FG-CLIP and FineCLIP and provide experimental results in the first response to reviewer 6zz8. We refer you to that response for more details.
__2. Response to Question... | Summary: The proposed method introduces Fine-Grained CLIP (FG-CLIP) for enhancing CLIP's fine-grained understanding capabilities. The authors propose three components to address this challenge: First, they generate 1.6 billion long caption-image pairs for capturing global-level semantic details. Second, they construct ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your constructive comments. We address your concerns below.
__1. Response to Questions 1-3 in Experimental Designs Or Analyses and Weakness 2__
Thank you for your comments regarding the improvement factor and fair comparison. The improvement of FG-CLIP stems f... | Summary: This paper proposes to fine-grained CLIP by introducing additional high-quality data and designing specific loss functions for training. As for the data, original CLIP only use short global caption data, while this work introduces long global caption, region-level caption, and region-level negative caption for... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback and recognizing the non-trivial contributions of our work. In response to your specific questions, we provide detailed explanations below, aiming to clarify any concerns.
__1. Response to questions in Experimental Designs Or Analyses__
Thanks for y... | null | null | null | null | null | null |
GTR: A General, Multi-View, and Dynamic Framework for Trajectory Representation Learning | Accept (poster) | Summary: This paper proposes GTR, a general, multi-view, dynamic framework for learning trajectory representation. The authors conduct a thorough review of existing studies and identify three critical limitations in current research: (1) reliance on single-view representations, (2) limited multitasking capabilities, an... | Rebuttal 1:
Rebuttal: **We appreciate the positive comments and our responses are detailed below.**
```
W1: Some parts of this paper are not explained very clearly, which could lead to misunderstandings and ambiguities. i) First, since there are many symbols, a symbol table is recommended for better following. ii) Bes... | Summary: This paper introduces GTR, a novel general, multi-view, and dynamic trajectory representation framework. GTR addresses the limitations of conventional approaches that rely exclusively on either free-space or road-network perspectives by incorporating a multi-view encoder to effectively capture the intrinsic sp... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for providing constructive feedback on our paper, and we greatly appreciate the acknowledgement of our contributions. We have addressed the specific concerns raised by the reviewer as detailed below.
```
W1&Q1: The paper does not specify the value of the b... | Summary: This paper proposes a novel framework for trajectory representation learning by integrating free-space trajectories with road network-based trajectories. The framework consists of three key components: 1) a multi-view encoder designed to handle different types of trajectories; 2) a spatial-temporal fusion pret... | Rebuttal 1:
Rebuttal: Thanks for all the valuable comments.
**Response to E1:** Existing works (START, Trembr, ST2Vec, etc.) mainly use Beijing and Porto datasets, so we adopted them for fair comparison. Following the suggestion, we have added the larger Chengdu dataset (containing 2,140,129 trajectories). Due to spac... | Summary: This paper proposes GTR, a trajectory representation framework built on a pre-train and fine-tune architecture.
The proposed GTR consists of a Multi-View Encoder (MVE) and Spatio-Temporal Fusion Mixture of Experts (ST-MoE), supports pre-training, fine-tuning, and Online Frozen-Hot Updating (OFU), and facilitat... | Rebuttal 1:
Rebuttal: **We thank the reviewer for offering the valuable feedback. We have addressed each of the concerns as outlined below.**
```
W1: Limited details are provided on the online updating approach.
```
We are happy to provide more details about the online updating approach. To process newly arrived traj... | null | null | null | null | null | null |
Instance-Optimal Pure Exploration for Linear Bandits on Continuous Arms | Accept (poster) | Summary: This paper studies $\epsilon$-BAI for Bayesian linear bandits with Gaussian noise and Gaussian prior on compact and continuous arm sets. The metric of performance that is being minimized is the posterior probability of identifying an $\epsilon$-optimal arm conditioned on the history of the observations. In par... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thorough and insightful review and apologize for any statements that may have caused confusion.
## Tractability of the algorithm
First, we note that our method is tractable in terms of the number of oracle calls. We do not specify algorithms (optimization or... | Summary: This paper investigates a pure exploration problem with linear bandit feedback on continuous arm sets, aiming to identify an $\varepsilon$-optimal arm with high probability. Previous approaches for continuous arm sets have employed instance-independent methods, due to technical challenges such as the infinite ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thorough and insightful review. We will revise our manuscript based on your suggestions.
## Readability
We appreciate reviewer's suggestion. In a revision, we will improve the readability of the paper (e.g. by making detailed statements in Prop 6.5 concise an... | Summary: This paper investigates the problem of best arm identification (BAI) for Bayesian linear bandits, where the action set is assumed to be continuous. While existing investigations, e.g., [Jedra et. al.] establish optimal algorithms when the set of arms is finite, BAI for linear bandits under infinite actions is ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thorough and insightful review. We will revise our manuscript based on your suggestions.
## Posterior probability of misidentification
As we briefly discussed in Line 133 (right), the posterior probability $P\_t(\zeta\_t \not \in \mathcal{X}^{\ast}(\epsilon)... | Summary: This work studies the problem of pure exploration for linear bandits particularly with continuous arms. The paper begins by establishing a lower bound on the asymptotic posterior probability of misidentification, and then proposes a tractable algorithm, called PMCA, for minimizing the error probability. The au... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thorough and insightful review. We will revise our manuscript based your on suggestions.
## Instance-dependent optimality and recommendation rule
We agree that while an asymptotically optimal algorithm needs both optimal sampling and recommendation rules, w... | null | null | null | null | null | null |
Reflection-Bench: Evaluating Epistemic Agency in Large Language Models | Accept (poster) | Summary: This paper proposes a cognitive-inspired benchmark, reflection-bench, to evaluate agency in LLMs. By decomposing the necessary cognitive procedures an agent would be required, the paper lists out seven important cognitive functions, including prediction, memory, belief updating, meta-reflection, and so on. The... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and specialist insights. We deeply value your expertise and have carefully considered each point of your feedback.
# "Agency" and its evaluation
We appreciate your insightful critique regarding our "agency" conceptual framework. Your concerns about the terminol... | Summary: The authors propose Reflection-Bench as a contamination-free benchmark consisting of seven parameterized cognitive tests inspired by cognitive psychology paradigms. The experimental evaluation spans 16 prominent LLMs and three prompting strategies: direct generation, free output, and Chain-of-Thought (CoT). Re... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and recognition of our benchmark's strengths. We have addressed your key concerns as follows:
# Evaluation across different difficulty levels
We fully agree with your suggestion about evaluating tasks at varying difficulty levels. In response, we have expanded ... | Summary: This paper proposes a benchmark to evaluate agency in large language models. The authors define agency along seven dimensions, namely prediction, decision-making, perception, memory, counterfactual thinking, belief updating, and meta-reflection. For each of these, the authors adapt a task from cognitive psycho... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and helpful suggestions. We've addressed your feedback as follows:
# Complementary experiments
Following your suggestion, we evaluated 18 models (original models plus two additional models: Centaur, fine-tuned with human cognitive tests performances [1], and... | Summary: This paper presents Reflection-Bench, a benchmark designed to evaluate the intrinsic agency of LLMs from seven cognitive dimensions: prediction, decision-making, perception, memory, counterfactual thinking, belief updating, and meta-reflection. The authors use or adapt a cognitive psychology-inspired and param... | Rebuttal 1:
Rebuttal: Thank you for your time and thoughtful suggestions. We've addressed your concerns as follows:
# Regarding "contamination-free" claims
We agree that our claim of "contamination-free" was overstated. We will revise all such instances throughout the paper to use more precise language such as "reduc... | null | null | null | null | null | null |
Global Convergence and Rich Feature Learning in $L$-Layer Infinite-Width Neural Networks under $\mu$ Parametrization | Accept (poster) | Summary: This paper investigates the training dynamics of infinitely wide neural networks with mup and SGD. They show that these neural networks can learn rich feature spaces and enjoy global convergence, which is better than other mainstream parameterizations such as NTK, MF, and SP. They also validate the theoretical... | Rebuttal 1:
Rebuttal: We thank the Reviewer for taking the time to review and give feedback on our manuscript. We appreciate the positive comments regarding the paper's clarity, the topic's significance, the originality of our ideas, and the supportive experimental results. We are particularly grateful for the reviewer... | Summary: The submitted paper analyzes the global convergence of MLPs in feature learning parameterization. By demonstrating that features remain independent during training, they prove global convergence.
Claims And Evidence: Yes. The theorems support the claims.
Methods And Evaluation Criteria: I am not sure if the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their support and constructive feedback. We address each question below:
---
**Q1**: Can the authors directly measure correlations between different features during training to support their analysis?
**A1**: Yes, we have directly measured the correlations between dif... | Summary: This paper studies the training of infinite-width $L$-layer FFN under the $\mu P$ parametrization. The authors establish that features evolve significantly during training while remaining linearly independent, ensuring convergence to a global minimum.
Claims And Evidence: The theoretical claims in the paper m... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. Due to space constraints, our responses are necessarily concise while addressing all key points:
---
**Q1**. How does your SGD analysis account for mini-batch randomness when your theoretical focus is on full-batch GD?
**A1**. Our analysis based on the Tens... | Summary: The paper aims to investigate rich feature learning and convergence to a global minimum via a Maximal Update Parametrization. Networks learn linearly independent features which are different from features at initialization, and due to covariance structure over layers, this implies a convergence to a global min... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback and valuable suggestions.
---
**Q1**. Lack of experiments across different architectures, layers, and seeds.
**A1**. We focused on MLPs as the conventional building blocks widely used in theoretical studies. While a full investigation acros... | null | null | null | null | null | null |
Active Learning with Selective Time-Step Acquisition for PDEs | Accept (poster) | Summary: This paper introduced an active learning method for learning PDEs. The method is composed of: (1) selective time-step acquisition, where the method selects a subset of time steps for the solver to simulate while other time steps are evolved by the surrogate model, (2) an acquisition function that evaluates the... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful questions and feedback.
> I wonder if the patterns shown in Figure 5 are consistent among different experiment runs (with different seeds)? The authors are encouraged to run multiple independent experiments, showing their patterns.
https://anonymous.4open.science/r/... | Summary: This paper introduces a novel active learning (AL) framework called Selective Time-Step Acquisition for PDEs (STAP) to improve the efficiency of surrogate models for partial differential equations (PDEs). The key idea is to selectively acquire only the most informative time steps from PDE trajectories using a ... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful questions and feedback.
> Prone to diverging models, producing out-of-distribution inputs
Thank you for raising this important point. Please see Common Response 2 at the bottom.
> Does not support autoregressive training techniques
We have done additional experime... | Summary: The paper introduces an active learning framework STAP for surrogate modeling of PDE trajectories that selectively queries only key time steps instead of simulating entire trajectories. STAP uses a binary sampling pattern to decide which time steps to acquire via a numerical solver and which to approximate wit... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful questions and feedback.
> Concerns about the lack of a direct comparison of computing resources with non-active learning
Our paper does not claim direct computational speedups on our benchmark PDEs; instead, it relies on the benchmark PDEs as proxies reflecting rea... | Summary: This paper develops an acquisition function that estimates the utility of a set of time steps and utilizes it for batch active learning in training surrogate models for PDEs. The empirical results show that the proposed method outperforms the baselines.
Claims And Evidence: The proposed algorithm relies on th... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful questions and feedback.
> Theoretical analysis of the acquisition function
Our acquisition function is an approximation to the expected error reduction (EER), which is statistically near-optimal for active learning [1][2]. The EER measures how much the model’s gener... | null | null | null | null | null | null |
In-Context Reinforcement Learning From Suboptimal Historical Data | Accept (poster) | Summary: The paper introduces a method for multi-task RL meta-learning with suboptimal behavior policies. The goal is to train a common transformer model to imitate and improve upon the observed behaviors to maximize online rewards in new tasks. Towards this end, the paper introduces two methods: 1. a weighted policy i... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. We hope our responses below address your concerns.
### Experiments
> **Bandit (Figure 2).** Our bandit experiments are designed to be comprehensive and aligned with established evaluation standards. As bandit problems are well-understood, ... | Summary: The new method Decision Importance Transformer (DIT) was proposed in the paper. This method is an enhancement of existing Decision Pretrained Transformer (DPT). While DPT requires expert target actions for training, DIT can be trained on trajectories sampled from suboptimal behavioral policies. Since there is... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. Please see our responses below to address your concerns.
### References
> We appreciate these shared references. We will include a discussion of AD-\epsilon, AWR, and AMAGO-2 into our manuscript. In particular, we highlight that AD-\epsilon still require... | Summary: This work focuses on in-context reinforcement learning where the source data/policy is suboptimal. In this case, the traditional ICRL algorithm could perform bad. This work proposes Decision Importance Transformer (DIT), which emulates the actor-critic algorithm in an in-context manner. It achieves superior pe... | Rebuttal 1:
Rebuttal: Thank you for your helpful and constructive comments. We will incorporate the referenced work into the final manuscript. In addition, we will include citations supporting the availability of abundant historical data in real-world settings (e.g., using [1] as a standard reference). Please see below... | Summary: The paper proposes the Decision Importance Transformer (DIT), a novel framework for in-context reinforcement learning (ICRL) that is designed to work with historical datasets generated by suboptimal behavioral policies. Unlike previous approaches that require optimal action labels or complete learning historie... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We hope our response can address your concerns.
### Presentation
> **Style Improvement.** We will follow the reviewer’s suggestion to remove most of the colored text, keeping only minimal phrases with key importance.
> **Clarification on the term “In-C... | null | null | null | null | null | null |
The Importance of Being Lazy: Scaling Limits of Continual Learning | Accept (poster) | Summary: This paper explores the relationship between scaling regimes and catastrophic forgetting using the lens of dynamical mean field theory (DMFT). The authors demonstrate theoretically that in feature learning regimes, catastrophic forgetting is more likely. In particular, there is a sharp transition between the l... | Rebuttal 1:
Rebuttal: *Thank you for taking the time to read and review our paper. We are glad to hear that you found our paper well-written and that it offers a novel contribution to the space of continual learning.*
---
## Edge of Laziness and Phase Transition
Thank you for your thoughtful comment, and we are sorr... | Summary: This paper studies how neural network parameterization (at the extremes, NTP and $\mu$P) shapes the effect of network width on catastrophic forgetting.
## Update after rebuttal
My assessment of the paper remains unchanged. I think this is an interesting contribution, but I am still skeptical of the added val... | Rebuttal 1:
Rebuttal: *We are grateful for your time reading our paper, and thank you for your thoughtful review.*
---
## The role of DMFT in the paper
We agree that the results of the DMFT are somewhat abstract, however we believe this is an inherent limitation of this theory: it is hard to simplify the results and... | Summary: This paper investigates the effect of model scale and the degree of feature learning in continual learning. It identifies a transition called Edge of Laziness influenced by task similarity, where the model exits an effectively lazy regime with low forgetting to enter a rich regime with significant forgetting. ... | Rebuttal 1:
Rebuttal: *Thank you for taking the time to read and review our paper. We are glad to hear that you found our paper clearly written, and that you consider our experiments well-designed, providing a systematic study of the topic and supporting well both claims and theoretical results.*
---
1. >**Do the cla... | Summary: The authors present a theoretical and experimental analysis of the effect of the neural network parametrisation on Catastrophic Forgetting.
The study extends previous works which focused on the lazy regime only.
The authors identify a spectrum of training regimes from the lazy regime to the feature learning re... | Rebuttal 1:
Rebuttal: *We sincerely thank you for taking the time to review our paper and for the thoughtful feedback. We are glad to hear that you found the work really interesting and stimulating. We are also grateful to hear that you appreciate our contribution to the literature.*
---
## The Pretraining Effect
We... | null | null | null | null | null | null |
FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for Enabling Fair LLM-Based Recommender Systems | Accept (poster) | Summary: The authors present a fairness-aware framework for LLM-based recommendation systems that combines conformal prediction with dynamic prompt engineering. FACTER introduces an adaptive semantic variance threshold and a violation-triggered mechanism to tighten fairness constraints when biases arise.
Claims And Ev... | Rebuttal 1:
Rebuttal: Thank you for carefully reading our work and your valuable comments. We address your concerns in the following paragraphs.
*Weakness 1:*
## A1.
While FACTER leverages existing techniques such as conformal prediction and prompt engineering, to our knowledge, __no prior work has unified these meth... | Summary: 1. This paper proposes FACTER, a fully post hoc framework that combines conformal thresholding and dynamic prompt engineering to address biases in black-box LLM-based recommender systems.
2. FACTER adaptively refines a fairness threshold via semantic variance checks and updates prompts whenever it detects vio... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment. We address your points in the following paragraphs.
*Weakness 1:*
## A1.
Our approach requires multiple hyper-parameters (e.g., $\lambda$, $\gamma$, $\tau_\rho$), which we tune via grid search on a 20% hold‑out calibration subset (see Section 4.2 and Appen... | Summary: In this paper, the authors propose FACTER (Fairness-Aware Conformal Thresholding and Prompt Engineering), a retrain-free framework that uses a designed non-conformity score and conformal prediction to dynamically adjust the fairness-aware prompts and mitigate fairness violations in LLM-based recommender system... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. We address your comments/concerns in the following paragraphs.
*Weakness 1*:
## A1.
Our theoretical guarantee (Theorem 1) assumes that the calibration set is approximately exchangeable with future test data. Hence, the quality and diversity of the calibration... | Summary: This paper proposes FACTER, a framework that integrates conformal prediction with iterative prompt engineering to mitigate demographic biases in recommender systems driven by large language models (LLMs). The authors introduce a notion of semantic variance as a proxy for identifying biased outputs when protect... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We address your comments in the following paragraphs.
*Weakness 1 and Q1*:
## A1.
As noted in Section 3.4, we acknowledge that any single embedding model can carry bias. Our theoretical analysis (Appendix §J.1.1, Theorem 1) shows that if embeddings drift by ... | null | null | null | null | null | null |
Return Capping: Sample Efficient CVaR Policy Gradient Optimisation | Accept (poster) | Summary: This paper presents a new method for optimizing Conditional Value at Risk (CVaR) in reinforcement learning using policy gradients. Traditional CVaR policy gradient methods suffer from poor sample efficiency because they discard a large proportion of trajectories. The authors propose Return Capping, a novel app... | Rebuttal 1:
Rebuttal: The main issue suggested in this review is flaws with the theoretical proof of the equivalence of Return Capping and standard CVaR PG optimisation. We would summarise the two points the reviewer raises as:
- Given Eqn (7), when $C$ is selected in the $\min(\cdot)$, this trajectory will have no eff... | Summary: The authors consider risk sensitive reinforcement learning where the goal is to optimize the $\alpha$-parameterized tail of the return distribution (on the lower end). Prior work proposed an approach known as conditional value at risk (CVaR) policy gradient, where the algorithm filters out all trajectories exc... | Rebuttal 1:
Rebuttal: In relation to the size of the environments presented, we have discussed this in our rebuttal to reviewer r3Le.
**Sample Efficiency**
For the specific question on sample efficiency, whilst there are improvements to sample efficiency using Return Capping compared to CVaR PG, it is unlikely to ac... | Summary: The authors address the problem of risk-sensitive policy optimization via policy gradient methods. They note several issues with the standard formulation of PG+CVaR which cause catastrophic losses in performance: specifically, because it discards the best trajectories by design, it is extremely difficult for a... | Rebuttal 1:
Rebuttal: Thank you for your time spent reviewing this paper and we appreciate your feedback
**Environment Complexity**
The main issue raised in this review is the limited complexity of the environments used. Whilst we agree that more complex environments would benefit the paper, as far as we are aware, t... | Summary: This paper proposes a novel method for CVaR optimization in Reinforcement Learning. The proposed method caps trajectory returns by a certain value and maximize its expected value with respect to a policy. It is theoretically shown that, the maximizer of the proposed objective matches the conventional optimal C... | Rebuttal 1:
Rebuttal: Thank you for referencing [3], we will include it in Related Work
**Setting Cap Minimum**
Addressing the question on setting $C^M$ in larger environments, it is very possible to set a suitable $C^M$, irrespective of the complexity of the environment.
We show in Proposition 1 that the Return Cap... | null | null | null | null | null | null |
Policy-Regret Minimization in Markov Games with Function Approximation | Accept (poster) | Summary: **Edit post-rebuttal: I thank the authors for their feedback, which answered my questions. I maintain my overall positive score.**
The submission considers Markov games, that is, MDPs where transitions depend on the pair of actions output by two players (the learner and the opponent). It studies a notion of r... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and the detailed comments.
---
> How close / related is the batching approach followed here, and formally used to get (2), to the one in Arora et al. (2012)? Somehow, I have the feeling that a part of the intuition remains: exploiting the m-bounded memory assu... | Summary: The paper introduces the first algorithmic framework for policy regret minimization in Markov games with general function approximation, achieving an $O\sqrt{T}$ policy regret bound for a wide range of problems. This framework extends to both large-scale environments with Eluder-type conditions and tabular cas... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback.
---
> I would like to confirm if adaptive adversary this paper is studying is just the standard adversary in robust RL via adversarial training, where players are modeled as max-min game.
No, the adaptive adversary in our paper adapts to the learner’... | Summary: This work explores policy regret minimization and proposes a new algorithm (BOVL) that is more general than past literature in that it deals with a class larger than tabular data Markov games. They use function approximation classes (characterized by Eluder type conditions) which can deal with larger state/act... | Rebuttal 1:
Rebuttal: Thank you for your feedback.
---
> Comparisons are carried out against it which is reasonable, but how does this work compares to other literature in Markov games and adversarial-opponent learning is not addressed.
Much of the prior work in Markov games and adversarial-opponent learning focus o... | null | null | null | null | null | null | null | null |
MoH: Multi-Head Attention as Mixture-of-Head Attention | Accept (poster) | Summary: This paper proposes Mixture-of-head attention (MoH) to replace standard multi-head attention(MHA) in transformers. The key idea is treat each attention head as an expert in mixture-of-expert framework. The experiments demonstrate that MoH can be applied to vision transformers(ViT) for image classification, DiT... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful comments and for recognizing our work as "intuitive and effective," acknowledging that "MoH can achieve competitive performance," and highlighting our "detailed ablation study and well-organized paper structure." Below, we address your questions in detail.
... | Summary: The paper proposes Mixture-of-Head (MoH), a replacement for the standard attention mechanism, in which attention heads can be adaptively switched on and off and reweighted for each token. This proposal is motivated by the already studied redundancy/specialization of attention heads, and the authors show that M... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful comments and for recognizing that "the idea of adding a MoE-like routing in the attention mechanism is fascinating and worth investigating, especially for efficiency purposes." Below, we address your questions in detail.
**Q1:** In most cases, performance e... | Summary: The paper proposes leveraging the Mixture-of-Experts (MoE) mechanism to upgrade the standard Multi-Head Attention into a novel Mixture-of-Heads (MoH) Attention. Specifically, MoH replaces the standard summation in multi-head attention with a weighted summation, where the weights are determined by a newly intro... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful comments and for recognizing that our method is a "practical contribution," "can be fine-tuned from pre-trained multi-head attention models," and "the results are promising and suggest broad applicability." Below, we address your questions in detail.
**Q1:*... | Summary: The paper introduces MoH (Mixture-of-Head Attention), a novel perspective on multi-head attention that formulates each head as an expert in a Mixture of Experts (MoE) framework. It employs a two-stage routing mechanism—comprising shared and non-shared experts—to reduce computational costs while enhancing accur... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful comments and your recognition that our method "provides a promising suggestion to advance attention-based models." Below, we provide detailed responses to your questions.
**Q1:** While prior work supports this claim when heads are combined through concatena... | Summary: The paper introduces Mixture-of-Head Attention (MoH) as an enhancement to the multi-head attention (MHA) mechanism in Transformer models, aiming to reduce computational costs while maintaining or improving model accuracy. The key insight is that not all attention heads contribute equally, and some can be prune... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments, and for noting that our method provides "a new perspective on structured sparsity in Transformers" and that "the significance is high." We address the questions as below.
**Q1:** If MoH is inspired by Mixture-of-Experts, then why does... | Summary: This paper aims to enhance the efficiency of multi-head self-attention by integrating mixture of experts into the attention. The authors propose Mixture of Head attention, which selectively activates subsets of attention heads for each token and gets a weighted sum of these selected heads to get the final outp... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful comments and for recognizing that "The experiments in the paper are well-conducted." Below, we address your questions in detail.
**Q1:** One weakness of the method is its relatively high activation rate.
**A1:** To demonstrate the robustness of our method,... | null | null |
CAD-Editor: A Locate-then-Infill Framework with Automated Training Data Synthesis for Text-Based CAD Editing | Accept (poster) | Summary: This paper introduces CAD-Editor, a framework for text-based CAD editing that leverages large language models (LLMs) through a locate-then-infill strategy for identifying modification areas and executing edits. To be more specific, CAD-Editor breaks down the editing process into two steps. First, it identifies... | Rebuttal 1:
Rebuttal: ## 1. Discussion on CAD Translator
We will include this work and other recent text-to-CAD efforts in the Related Work section. Since CAD Translator does not take existing CAD models as input, it cannot directly handle the text-based editing task discussed here. Additionally, its code is not publi... | Summary: This paper introduces CAD-Editor, the first framework for text-based CAD editing. The authors frame the problem as a sequence-to-sequence generation task and propose a locate-then-infill approach that decomposes editing into two sub-tasks: locating regions requiring modification and infilling these regions wit... | Rebuttal 1:
Rebuttal: ## 1. Ambiguous Editing Instructions
We agree that handling ambiguous editing instructions is a critical challenge. However, ambiguity in natural language is a long-standing issue in NLP research [1]. As the first work on natural language-driven CAD editing, our focus is on establishing a complet... | Summary: This paper introduces a text-based CAD editing framework. The authors propose an automated data synthesis pipeline that generates triplet data with VLMs and variation models. They designed a locate-then-infill framework to perform the editing process.
Claims And Evidence: 1. Automated data synthesis pipeline:... | Rebuttal 1:
Rebuttal: ## 1. Edit the Starting of a CAD Sequence
1. To support edits at diverse positions, we adopt a reversible annotation strategy as described in Sec.4: for each training pair, we also include a version with edited sequence and original sequence swapped, encouraging the model to generalize across edi... | Summary: Authors propose a novel task for text-based CAD editing of sketch-and-extrude models. They demonstrate that LVLMs can be used to annotate the editing instructions. A synthesis CAD editing dataset is proposed based on DeepCAD dataset. Finally a locate-then-infill framework is proposed to generate the edited CAD... | Rebuttal 1:
Rebuttal: ## 1. More Details on Using HNC-CAD to Generate Design Variations
We follow the original implementation of HNC-CAD when setting the retained part of the original model and the sampling threshold.
- Only one sketch-extrude (SE) component from the original model is retained, while all other SE com... | null | null | null | null | null | null |
Square$\chi$PO: Differentially Private and Robust $\chi^2$-Preference Optimization in Offline Direct Alignment | Accept (poster) | Summary: This paper studies the problem of alignment of language models with preference feedback, under two variations: (i) label corruption and (ii) privacy protections. While motivated by language models, there is nothing is specific to language models in the techniques, and they are more generally applicable to any ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback and constructive suggestions. We address the main points below and hope it will help to resolve your concerns.
**1. Motivation of LTC.** The main motivation behind LTC is that after users privatize their preferences, the collected preference signal... | Summary: The paper studies algorithms for alignment given privacy and robustness considerations. In alignment we are given examples x, two model responses $a_0, a_1$, and a label $y$ denoting that $a_y$ was preferred to $a_{1-y}$. The labels are generated under one of two models, a reward model where each label-respons... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation of our paper. We appreciate your recognition of our general analysis with non-trivial modifications of previous results.
We are also grateful for your recognition of our transparency in acknowledging the computational efficiency limitations in the central m... | Summary: The paper proposes differentially private and robust offline preference alignment with human feedback. The method is based on the prior work of $\chi$PO, but uses square loss instead of the log loss.
Claims And Evidence: The paper claims to achieve optimal rates in general function approximations under privac... | Rebuttal 1:
Rebuttal: Thank you for time and feedback. We will recap your comments and present our detailed response. We hope our answers will resolve your concerns.
**1. Significance of the difference compared to $\chi$PO**
We clarify that the key significance and benefits of moving from log-loss to square loss in o... | Summary: LLMs is important to have alignment with human response. This work focuses on an approach from direct preference optimization, especially on CHI-PO. This approach is to address overoptimization issue in DPO on single-policy concentrability, which is a kind of offline alignment approach. In such approach, priva... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation of our paper. We appreciate your recognition of our theoretical contributions to offline alignment, as well as our approach to privacy and robustness.
We're also grateful for your appreciation of a purely theoretical paper. To further support our main result... | null | null | null | null | null | null |
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